MRNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VgPa4TB6FGYm"
   },
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import glob\n",
    "from sklearn.preprocessing import normalize\n",
    "import gc\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.xception import Xception\n",
    "from keras.layers import Input, Dense, Flatten, GlobalAveragePooling2D, GlobalMaxPooling2D \n",
    "from keras.models import Model\n",
    "from keras.optimizers import SGD\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint, Callback\n",
    "from keras.models import load_model\n",
    "from keras.layers import Dropout\n",
    "from keras import backend as K\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 128
    },
    "colab_type": "code",
    "id": "wr4vmtAHFWd6",
    "outputId": "237a5dba-aa68-448a-f014-1bb0160d66d9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "drive.mount('/content/drive')\n",
    "root_path = 'drive/My Drive/Colab Notebooks/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "IGZvfyYLFfOA",
    "outputId": "a9f8ea46-9866-4bd1-8572-7314159319b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/drive/My Drive/Colab Notebooks\n"
     ]
    }
   ],
   "source": [
    "cd drive/My Drive/Colab Notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0IZsEifGF6xD"
   },
   "outputs": [],
   "source": [
    "#load the training data of one single angle of the exams (axial | coronal | sagittal) by giving the angle name\n",
    "def trainingSet(file_name):\n",
    "    training_set = []\n",
    "    for np_name in sorted(glob.glob('MRNet-v1.0/train/'+file_name+'/*.npy')):\n",
    "        training_set.append(np.load(np_name))\n",
    "    return training_set    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hDEOQ3sUG-JL"
   },
   "outputs": [],
   "source": [
    "#load the labels of one knee tear(abnormal | acl | meniscus) by the giving the file name that contains the labels\n",
    "def load_labels(train_file_name):\n",
    "    training_labels = np.loadtxt(train_file_name, delimiter=',')[:,1]\n",
    "    return training_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MA0X0JzCNfXX"
   },
   "outputs": [],
   "source": [
    "#as each exam (stack) contains S slices and most of the pretrained CNN models accept the image with channel = 3\n",
    "#extract the middle 3 slices as most of the time they deside if the patiant has a certain tear or not\n",
    "def extract_3(dataset, data_size):\n",
    "    for i in range(len(dataset)):\n",
    "        dataset[i] = list(dataset[i])\n",
    "        middel_idx = int(len(dataset[i]) / 2)\n",
    "        dataset[i] = dataset[i][middel_idx - 1 : middel_idx + 2]\n",
    "        dataset[i] = np.array(dataset[i])\n",
    "    dataset = np.array(dataset)\n",
    "    dataset = dataset.reshape(data_size, 256, 256 ,3)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w_hU0XOUGhby"
   },
   "outputs": [],
   "source": [
    "#load the training data for each angle of the knee\n",
    "training_set_axial= trainingSet('axial')\n",
    "training_set_coronal=trainingSet('coronal')\n",
    "training_set_sagittal=trainingSet('sagittal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fnjEyVtvN8mS"
   },
   "outputs": [],
   "source": [
    "#extract the middel 3 slices from each stack\n",
    "training_set_axial = extract_3(training_set_axial, 1130)\n",
    "training_set_coronal =extract_3(training_set_coronal, 1130)\n",
    "training_set_sagittal =extract_3(training_set_sagittal, 1130)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "4oOEuN-VTVYB",
    "outputId": "2bf4279b-ce34-47cd-9e98-79da3edd0cff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1130, 256, 256, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set_axial.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5-fzezVNHInf"
   },
   "outputs": [],
   "source": [
    "#load the labels of the trainig data for each knee tear \n",
    "training_label_abnormal = load_labels('MRNet-v1.0/train-abnormal.csv')\n",
    "training_label_acl = load_labels('MRNet-v1.0/train-acl.csv')\n",
    "training_label_meniscus = load_labels('MRNet-v1.0/train-meniscus.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
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     "height": 35
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    "colab_type": "code",
    "id": "soOp5GPoTbOh",
    "outputId": "4e316f3e-9251-4ada-bd8a-b24342c76bf6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1130,)"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_label_acl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
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     "height": 5383
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    "id": "9fXZ-LbeKhR0",
    "outputId": "608e813d-b4f5-43c8-ae3b-d6593cfb635b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            (None, 256, 256, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1 (Conv2D)           (None, 127, 127, 32) 864         input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1_bn (BatchNormaliza (None, 127, 127, 32) 128         block1_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1_act (Activation)   (None, 127, 127, 32) 0           block1_conv1_bn[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2 (Conv2D)           (None, 125, 125, 64) 18432       block1_conv1_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2_bn (BatchNormaliza (None, 125, 125, 64) 256         block1_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2_act (Activation)   (None, 125, 125, 64) 0           block1_conv2_bn[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv1 (SeparableConv2 (None, 125, 125, 128 8768        block1_conv2_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv1_bn (BatchNormal (None, 125, 125, 128 512         block2_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2_act (Activation (None, 125, 125, 128 0           block2_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2 (SeparableConv2 (None, 125, 125, 128 17536       block2_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2_bn (BatchNormal (None, 125, 125, 128 512         block2_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_99 (Conv2D)              (None, 63, 63, 128)  8192        block1_conv2_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block2_pool (MaxPooling2D)      (None, 63, 63, 128)  0           block2_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_99 (BatchNo (None, 63, 63, 128)  512         conv2d_99[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 63, 63, 128)  0           block2_pool[0][0]                \n",
      "                                                                 batch_normalization_99[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1_act (Activation (None, 63, 63, 128)  0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1 (SeparableConv2 (None, 63, 63, 256)  33920       block3_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1_bn (BatchNormal (None, 63, 63, 256)  1024        block3_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2_act (Activation (None, 63, 63, 256)  0           block3_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2 (SeparableConv2 (None, 63, 63, 256)  67840       block3_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2_bn (BatchNormal (None, 63, 63, 256)  1024        block3_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_100 (Conv2D)             (None, 32, 32, 256)  32768       add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block3_pool (MaxPooling2D)      (None, 32, 32, 256)  0           block3_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_100 (BatchN (None, 32, 32, 256)  1024        conv2d_100[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 32, 32, 256)  0           block3_pool[0][0]                \n",
      "                                                                 batch_normalization_100[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1_act (Activation (None, 32, 32, 256)  0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1 (SeparableConv2 (None, 32, 32, 728)  188672      block4_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1_bn (BatchNormal (None, 32, 32, 728)  2912        block4_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2_act (Activation (None, 32, 32, 728)  0           block4_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2 (SeparableConv2 (None, 32, 32, 728)  536536      block4_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2_bn (BatchNormal (None, 32, 32, 728)  2912        block4_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_101 (Conv2D)             (None, 16, 16, 728)  186368      add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block4_pool (MaxPooling2D)      (None, 16, 16, 728)  0           block4_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_101 (BatchN (None, 16, 16, 728)  2912        conv2d_101[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 16, 16, 728)  0           block4_pool[0][0]                \n",
      "                                                                 batch_normalization_101[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1_act (Activation (None, 16, 16, 728)  0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1 (SeparableConv2 (None, 16, 16, 728)  536536      block5_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1_bn (BatchNormal (None, 16, 16, 728)  2912        block5_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2_act (Activation (None, 16, 16, 728)  0           block5_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2 (SeparableConv2 (None, 16, 16, 728)  536536      block5_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2_bn (BatchNormal (None, 16, 16, 728)  2912        block5_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3_act (Activation (None, 16, 16, 728)  0           block5_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3 (SeparableConv2 (None, 16, 16, 728)  536536      block5_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3_bn (BatchNormal (None, 16, 16, 728)  2912        block5_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 16, 16, 728)  0           block5_sepconv3_bn[0][0]         \n",
      "                                                                 add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv1_act (Activation (None, 16, 16, 728)  0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv1 (SeparableConv2 (None, 16, 16, 728)  536536      block6_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv1_bn (BatchNormal (None, 16, 16, 728)  2912        block6_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2_act (Activation (None, 16, 16, 728)  0           block6_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2 (SeparableConv2 (None, 16, 16, 728)  536536      block6_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2_bn (BatchNormal (None, 16, 16, 728)  2912        block6_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3_act (Activation (None, 16, 16, 728)  0           block6_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3 (SeparableConv2 (None, 16, 16, 728)  536536      block6_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3_bn (BatchNormal (None, 16, 16, 728)  2912        block6_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_17 (Add)                    (None, 16, 16, 728)  0           block6_sepconv3_bn[0][0]         \n",
      "                                                                 add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1_act (Activation (None, 16, 16, 728)  0           add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1 (SeparableConv2 (None, 16, 16, 728)  536536      block7_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1_bn (BatchNormal (None, 16, 16, 728)  2912        block7_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2_act (Activation (None, 16, 16, 728)  0           block7_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2 (SeparableConv2 (None, 16, 16, 728)  536536      block7_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2_bn (BatchNormal (None, 16, 16, 728)  2912        block7_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3_act (Activation (None, 16, 16, 728)  0           block7_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3 (SeparableConv2 (None, 16, 16, 728)  536536      block7_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3_bn (BatchNormal (None, 16, 16, 728)  2912        block7_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_18 (Add)                    (None, 16, 16, 728)  0           block7_sepconv3_bn[0][0]         \n",
      "                                                                 add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1_act (Activation (None, 16, 16, 728)  0           add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1 (SeparableConv2 (None, 16, 16, 728)  536536      block8_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1_bn (BatchNormal (None, 16, 16, 728)  2912        block8_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2_act (Activation (None, 16, 16, 728)  0           block8_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2 (SeparableConv2 (None, 16, 16, 728)  536536      block8_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2_bn (BatchNormal (None, 16, 16, 728)  2912        block8_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3_act (Activation (None, 16, 16, 728)  0           block8_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3 (SeparableConv2 (None, 16, 16, 728)  536536      block8_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3_bn (BatchNormal (None, 16, 16, 728)  2912        block8_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_19 (Add)                    (None, 16, 16, 728)  0           block8_sepconv3_bn[0][0]         \n",
      "                                                                 add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1_act (Activation (None, 16, 16, 728)  0           add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1 (SeparableConv2 (None, 16, 16, 728)  536536      block9_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1_bn (BatchNormal (None, 16, 16, 728)  2912        block9_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2_act (Activation (None, 16, 16, 728)  0           block9_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2 (SeparableConv2 (None, 16, 16, 728)  536536      block9_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2_bn (BatchNormal (None, 16, 16, 728)  2912        block9_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3_act (Activation (None, 16, 16, 728)  0           block9_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3 (SeparableConv2 (None, 16, 16, 728)  536536      block9_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3_bn (BatchNormal (None, 16, 16, 728)  2912        block9_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_20 (Add)                    (None, 16, 16, 728)  0           block9_sepconv3_bn[0][0]         \n",
      "                                                                 add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1_act (Activatio (None, 16, 16, 728)  0           add_20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1 (SeparableConv (None, 16, 16, 728)  536536      block10_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1_bn (BatchNorma (None, 16, 16, 728)  2912        block10_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2_act (Activatio (None, 16, 16, 728)  0           block10_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2 (SeparableConv (None, 16, 16, 728)  536536      block10_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2_bn (BatchNorma (None, 16, 16, 728)  2912        block10_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3_act (Activatio (None, 16, 16, 728)  0           block10_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3 (SeparableConv (None, 16, 16, 728)  536536      block10_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3_bn (BatchNorma (None, 16, 16, 728)  2912        block10_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_21 (Add)                    (None, 16, 16, 728)  0           block10_sepconv3_bn[0][0]        \n",
      "                                                                 add_20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1_act (Activatio (None, 16, 16, 728)  0           add_21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1 (SeparableConv (None, 16, 16, 728)  536536      block11_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1_bn (BatchNorma (None, 16, 16, 728)  2912        block11_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2_act (Activatio (None, 16, 16, 728)  0           block11_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2 (SeparableConv (None, 16, 16, 728)  536536      block11_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2_bn (BatchNorma (None, 16, 16, 728)  2912        block11_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3_act (Activatio (None, 16, 16, 728)  0           block11_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3 (SeparableConv (None, 16, 16, 728)  536536      block11_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3_bn (BatchNorma (None, 16, 16, 728)  2912        block11_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_22 (Add)                    (None, 16, 16, 728)  0           block11_sepconv3_bn[0][0]        \n",
      "                                                                 add_21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1_act (Activatio (None, 16, 16, 728)  0           add_22[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1 (SeparableConv (None, 16, 16, 728)  536536      block12_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1_bn (BatchNorma (None, 16, 16, 728)  2912        block12_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2_act (Activatio (None, 16, 16, 728)  0           block12_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2 (SeparableConv (None, 16, 16, 728)  536536      block12_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2_bn (BatchNorma (None, 16, 16, 728)  2912        block12_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3_act (Activatio (None, 16, 16, 728)  0           block12_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3 (SeparableConv (None, 16, 16, 728)  536536      block12_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3_bn (BatchNorma (None, 16, 16, 728)  2912        block12_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_23 (Add)                    (None, 16, 16, 728)  0           block12_sepconv3_bn[0][0]        \n",
      "                                                                 add_22[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1_act (Activatio (None, 16, 16, 728)  0           add_23[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1 (SeparableConv (None, 16, 16, 728)  536536      block13_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1_bn (BatchNorma (None, 16, 16, 728)  2912        block13_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2_act (Activatio (None, 16, 16, 728)  0           block13_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2 (SeparableConv (None, 16, 16, 1024) 752024      block13_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2_bn (BatchNorma (None, 16, 16, 1024) 4096        block13_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_102 (Conv2D)             (None, 8, 8, 1024)   745472      add_23[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block13_pool (MaxPooling2D)     (None, 8, 8, 1024)   0           block13_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_102 (BatchN (None, 8, 8, 1024)   4096        conv2d_102[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_24 (Add)                    (None, 8, 8, 1024)   0           block13_pool[0][0]               \n",
      "                                                                 batch_normalization_102[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1 (SeparableConv (None, 8, 8, 1536)   1582080     add_24[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1_bn (BatchNorma (None, 8, 8, 1536)   6144        block14_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1_act (Activatio (None, 8, 8, 1536)   0           block14_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2 (SeparableConv (None, 8, 8, 2048)   3159552     block14_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2_bn (BatchNorma (None, 8, 8, 2048)   8192        block14_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2_act (Activatio (None, 8, 8, 2048)   0           block14_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling2d_3 (GlobalM (None, 2048)         0           block14_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "dense_9 (Dense)                 (None, 1024)         2098176     global_max_pooling2d_3[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_10 (Dense)                (None, 128)          131200      dense_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_11 (Dense)                (None, 32)           4128        dense_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_12 (Dense)                (None, 1)            33          dense_11[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 23,095,017\n",
      "Trainable params: 23,040,489\n",
      "Non-trainable params: 54,528\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n"
     ]
    }
   ],
   "source": [
    "#load INCEPTIONV3 pretrainned model from keras without the top layer &\n",
    "#specify the shape of the input image as (256x256x3) \n",
    "mri_InceptionV3 = InceptionV3(include_top = False, input_shape = (256, 256, 3))\n",
    "\n",
    "#remove the last one\n",
    "mri_InceptionV3.layers.pop()\n",
    "\n",
    "#get the output of the INCEPTIONV# model \"feature extraction\" and add Dense layers so that the model is designed\n",
    "#to classify our data\n",
    "mri_InceptionV3_output = mri_InceptionV3.output\n",
    "\n",
    "mri_InceptionV3_last_layer = GlobalMaxPooling2D()(mri_InceptionV3_output)\n",
    "\n",
    "mri_InceptionV3_last_layer = Dense(1024, activation = 'relu')(mri_InceptionV3_last_layer)\n",
    "\n",
    "mri_InceptionV3_last_layer = Dense(128, activation = 'relu')(mri_InceptionV3_last_layer)\n",
    "\n",
    "mri_InceptionV3_last_layer = Dense(32, activation = 'relu')(mri_InceptionV3_last_layer)\n",
    "\n",
    "predictor = Dense(1, activation='sigmoid')(mri_InceptionV3_last_layer)\n",
    "\n",
    "modified_mri_InceptionV3 = Model(inputs = mri_InceptionV3.input, output = predictor)\n",
    "\n",
    "modified_mri_InceptionV3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "h_Ex8umhLHRf"
   },
   "outputs": [],
   "source": [
    "#freeze the added dense layers and unfreeze the rest:\n",
    "for layer in modified_mri_InceptionV3.layers[15:]:\n",
    "    layer.trainable = True \n",
    "    \n",
    "for layer in mri_InceptionV3.layers:\n",
    "    layer.trainable = False\n",
    "    \n",
    "modified_mri_InceptionV3.compile(loss='binary_crossentropy', optimizer=\"adam\", metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QW0nkyYphAXF"
   },
   "outputs": [],
   "source": [
    "class Metrics(Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.val_f1s = []\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        val_predict = (np.asarray(self.model.predict(self.validation_data[0]))).round()\n",
    "        val_targ = self.validation_data[1]\n",
    "        _val_f1 = f1_score(val_targ, val_predict)\n",
    "        self.val_f1s.append(_val_f1)\n",
    "        print ('-val_f1 : %f '%(_val_f1))\n",
    "        return\n",
    "metrics = Metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YsweVLh8p68x"
   },
   "outputs": [],
   "source": [
    "def plot_history(history):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    ax = plt.gca()\n",
    "    plt.title('Training performance')\n",
    "    plt.plot(history.epoch, history.history['loss'], label='Loss')\n",
    "    plt.plot(history.epoch, history.history['val_loss'], label='validation error')\n",
    "    plt.plot(history.epoch, history.history['acc'], label='accuracy')\n",
    "    plt.plot(history.epoch, history.history['val_acc'], label='validation accuracy')\n",
    "    plt.plot(metrics.val_f1s, label='f_score')\n",
    "    plt.grid(True,color='white')\n",
    "    mpl.style.use('seaborn')\n",
    "    plt.legend() \n",
    "          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qy5DNM96LedU"
   },
   "outputs": [],
   "source": [
    "#train the model with 1073 trainning data & 57 validation data\n",
    "#save the model that has the max validation accuracy\n",
    "#at the end plot the trainng loss & accuracy, validation loss & accuracy & F-score \n",
    "def fit_model(training_set, training_labels, model_file_name):\n",
    "  \n",
    "    history = modified_mri_InceptionV3.fit(x = training_set, y = training_labels, batch_size=32,\n",
    "                       epochs=30, validation_split = 0.05,\n",
    "                       callbacks =[\n",
    "                          EarlyStopping(patience=3, monitor = 'val_loss', mode = 'min'),\n",
    "                          ModelCheckpoint(model_file_name, monitor='val_acc', save_best_only=True, mode='max', period=1), metrics])\n",
    "    plot_history(history)\n",
    "    best_model = load_model(model_file_name)\n",
    "    return best_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 789
    },
    "colab_type": "code",
    "id": "yJMZ8-NMLon_",
    "outputId": "9ba37079-067d-4196-c59a-5d17c45f272c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1073 samples, validate on 57 samples\n",
      "Epoch 1/30\n",
      "1073/1073 [==============================] - 51s 48ms/step - loss: 0.7450 - acc: 0.7409 - val_loss: 1.9503 - val_acc: 0.8421\n",
      "-val_f1 : 0.914286 \n",
      "Epoch 2/30\n",
      "1073/1073 [==============================] - 7s 6ms/step - loss: 0.5043 - acc: 0.7894 - val_loss: 1.2111 - val_acc: 0.8246\n",
      "-val_f1 : 0.903846 \n",
      "Epoch 3/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4911 - acc: 0.7829 - val_loss: 2.3140 - val_acc: 0.8421\n",
      "-val_f1 : 0.914286 \n",
      "Epoch 4/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5190 - acc: 0.7866 - val_loss: 0.9788 - val_acc: 0.7193\n",
      "-val_f1 : 0.833333 \n",
      "Epoch 5/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.3996 - acc: 0.8257 - val_loss: 2.2516 - val_acc: 0.8421\n",
      "-val_f1 : 0.914286 \n",
      "Epoch 6/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.3655 - acc: 0.8416 - val_loss: 1.3464 - val_acc: 0.7895\n",
      "-val_f1 : 0.882353 \n",
      "Epoch 7/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.3678 - acc: 0.8444 - val_loss: 1.7341 - val_acc: 0.8246\n",
      "-val_f1 : 0.903846 \n"
     ]
    },
    {
     "data": {
      "image/png": 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58+flvzieGUmo9zicLBzv+lhH917k0tmWvX/drbeKYSG3Tu2PGjWWI0cOMmPG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\nJM/8f8nm5e/d5oouAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train the modified INCEPTIONV3 model that will accept the axial data and classify the knee is it has an abnormal tear or not \n",
    "axial_abnormal_model = fit_model(training_set_axial, training_label_abnormal, \"axial_abnormal_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 736
    },
    "colab_type": "code",
    "id": "rgwq4QabMM3f",
    "outputId": "413de2f6-95ed-4f13-f28c-99b332be5291"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1073 samples, validate on 57 samples\n",
      "Epoch 1/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.6046 - acc: 0.7819 - val_loss: 1.3688 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 2/30\n",
      "  32/1073 [..............................] - ETA: 6s - loss: 0.6519 - acc: 0.7188"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4726 - acc: 0.8034 - val_loss: 0.9485 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 3/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4693 - acc: 0.8127 - val_loss: 1.5979 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 4/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4114 - acc: 0.8276 - val_loss: 1.8333 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 5/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4196 - acc: 0.8313 - val_loss: 1.8349 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n"
     ]
    },
    {
     "data": {
      "image/png": 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VKgWWKYrCwIGDGT9+FHXr1qdx46YF2u/ePZRXX32JkyeP07//I/j6+rJu3Rpe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\nA5s76KYCWbz4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train the modified INCEPTIONV3 model that will accept the axial data and classify the knee is it has an acl tear or not \n",
    "axial_acl_model = fit_model(training_set_axial, training_label_acl, \"axial_acl_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1009
    },
    "colab_type": "code",
    "id": "n6koKwqZTiIR",
    "outputId": "7ac63df0-8612-43f5-88f9-e91642321a70"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1073 samples, validate on 57 samples\n",
      "Epoch 1/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.6739 - acc: 0.6505 - val_loss: 1.1731 - val_acc: 0.6140\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 2/30\n",
      "  32/1073 [..............................] - ETA: 6s - loss: 0.5649 - acc: 0.7188"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5903 - acc: 0.6934 - val_loss: 1.2215 - val_acc: 0.6842\n",
      "-val_f1 : 0.400000 \n",
      "Epoch 3/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5974 - acc: 0.6710 - val_loss: 1.3562 - val_acc: 0.6316\n",
      "-val_f1 : 0.086957 \n",
      "Epoch 4/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5609 - acc: 0.7092 - val_loss: 1.1594 - val_acc: 0.6667\n",
      "-val_f1 : 0.512821 \n",
      "Epoch 5/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5627 - acc: 0.7120 - val_loss: 2.7350 - val_acc: 0.6140\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 6/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5753 - acc: 0.6999 - val_loss: 1.7573 - val_acc: 0.6491\n",
      "-val_f1 : 0.285714 \n",
      "Epoch 7/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5655 - acc: 0.7083 - val_loss: 1.1494 - val_acc: 0.5789\n",
      "-val_f1 : 0.538462 \n",
      "Epoch 8/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5393 - acc: 0.7288 - val_loss: 1.6312 - val_acc: 0.6491\n",
      "-val_f1 : 0.411765 \n",
      "Epoch 9/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5473 - acc: 0.7269 - val_loss: 1.6303 - val_acc: 0.6491\n",
      "-val_f1 : 0.375000 \n",
      "Epoch 10/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5326 - acc: 0.7390 - val_loss: 1.3591 - val_acc: 0.5965\n",
      "-val_f1 : 0.439024 \n"
     ]
    },
    {
     "data": {
      "image/png": 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iYqJZsGABanXFmZhu3boTHNyRp556BL1ez+OPz8LGpvYJTe666x7effctvL1b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\nSJJEaOAN6IH1pI5/g9z481QLY/SP3tW1D7l6dZrJw1/EMgrERm4j1P/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      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train the modified INCEPTIONV3 model that will accept the axial data and classify the knee is it has a meniscus tear or not \n",
    "axial_meniscus_model = fit_model(training_set_axial, training_label_meniscus, \"axial_meniscus_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 735
    },
    "colab_type": "code",
    "id": "IaMWyKecTiWD",
    "outputId": "3346ef4f-5b48-44b7-8a0d-3401bc547a53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1073 samples, validate on 57 samples\n",
      "Epoch 1/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5165 - acc: 0.7791 - val_loss: 1.2942 - val_acc: 0.8421\n",
      "-val_f1 : 0.914286 \n",
      "Epoch 2/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4605 - acc: 0.8089 - val_loss: 1.4683 - val_acc: 0.8421\n",
      "-val_f1 : 0.914286 \n",
      "Epoch 3/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4135 - acc: 0.8127 - val_loss: 0.9535 - val_acc: 0.8421\n",
      "-val_f1 : 0.914286 \n",
      "Epoch 4/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.3946 - acc: 0.8034 - val_loss: 1.5497 - val_acc: 0.8421\n",
      "-val_f1 : 0.914286 \n",
      "Epoch 5/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.3907 - acc: 0.8062 - val_loss: 1.7496 - val_acc: 0.8421\n",
      "-val_f1 : 0.914286 \n",
      "Epoch 6/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.3551 - acc: 0.8248 - val_loss: 2.0481 - val_acc: 0.8421\n",
      "-val_f1 : 0.914286 \n"
     ]
    },
    {
     "data": {
      "image/png": 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ZXtBo5En/8fLBT4g7kOJcjeUa8jiacJL9MYe4kHoZADuNLd18OxPo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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train the modified INCEPTIONV3 model that will accept the coronal data and classify the knee is it has an abnormal tear or not \n",
    "coronal_abnormal_model = fit_model(training_set_coronal, training_label_abnormal, \"coronal_abnormal_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 845
    },
    "colab_type": "code",
    "id": "YFEUHQozTik8",
    "outputId": "2d25874e-4c50-4f8a-9978-d04c472dca7e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1073 samples, validate on 57 samples\n",
      "Epoch 1/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5664 - acc: 0.7875 - val_loss: 1.1491 - val_acc: 0.8421\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-val_f1 : 0.000000 \n",
      "Epoch 2/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5084 - acc: 0.8145 - val_loss: 1.6462 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 3/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4874 - acc: 0.8145 - val_loss: 1.6189 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 4/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4974 - acc: 0.8145 - val_loss: 0.9538 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 5/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4903 - acc: 0.8145 - val_loss: 1.0923 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 6/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4881 - acc: 0.8145 - val_loss: 1.3405 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 7/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4867 - acc: 0.8145 - val_loss: 1.3782 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n"
     ]
    },
    {
     "data": {
      "image/png": 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0aNGS11//D7a2tuh0xSQnJ+Hp6cWePX/y1lsLqvgfEcLySHGuQatO/8KlvET6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\nAGCY/wdvFK0yOIIM5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train the modified INCEPTIONV3 model that will accept the coronal data and classify the knee is it has an acl tear or not \n",
    "coronal_acl_model = fit_model(training_set_coronal, training_label_acl, \"coronal_acl_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 682
    },
    "colab_type": "code",
    "id": "cN3i8KC4TixN",
    "outputId": "0e456835-0547-4a17-b718-abf4d40956fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1073 samples, validate on 57 samples\n",
      "Epoch 1/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.6481 - acc: 0.6505 - val_loss: 1.6396 - val_acc: 0.6140\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 2/30\n",
      "  32/1073 [..............................] - ETA: 6s - loss: 0.5691 - acc: 0.7500"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.6151 - acc: 0.6598 - val_loss: 2.2550 - val_acc: 0.6140\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 3/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.6147 - acc: 0.6542 - val_loss: 2.4030 - val_acc: 0.6140\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 4/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5974 - acc: 0.6664 - val_loss: 3.2045 - val_acc: 0.6140\n",
      "-val_f1 : 0.000000 \n"
     ]
    },
    {
     "data": {
      "image/png": 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7adeuPb/9dshjmmmaGIZBbm4uubm57N37s9ftt259HjabjbVrv6SgoACA9u07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\njzX0UPOpbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train the modified INCEPTIONV3 model that will accept the coronal data and classify the knee is it has a meniscus tear or not \n",
    "coronal_meniscus_model = fit_model(training_set_coronal, training_label_meniscus, \"coronal_meniscus_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 680
    },
    "colab_type": "code",
    "id": "H_YrRaorTi9t",
    "outputId": "6f9066fd-4b33-4306-f8c5-188218bc3e67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1073 samples, validate on 57 samples\n",
      "Epoch 1/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5570 - acc: 0.7465 - val_loss: 1.0596 - val_acc: 0.8421\n",
      "-val_f1 : 0.914286 \n",
      "Epoch 2/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4722 - acc: 0.8062 - val_loss: 0.6113 - val_acc: 0.8421\n",
      "-val_f1 : 0.914286 \n",
      "Epoch 3/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4481 - acc: 0.8108 - val_loss: 0.9096 - val_acc: 0.8421\n",
      "-val_f1 : 0.914286 \n",
      "Epoch 4/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.3985 - acc: 0.8276 - val_loss: 0.9255 - val_acc: 0.8246\n",
      "-val_f1 : 0.901961 \n",
      "Epoch 5/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.3808 - acc: 0.8378 - val_loss: 0.9157 - val_acc: 0.5263\n",
      "-val_f1 : 0.649351 \n"
     ]
    },
    {
     "data": {
      "image/png": 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GSwLOnCdRDVbqzn2Oq/GE3iWJbhj1LuB2+P0BDu4po662nx6srsWR2ziBsso6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\nBvheoLbBA1y/NeBA+Vw6O1p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J1oHd1lSIzhRFYd4DecQlWNm34xwlp/Rt5HFJXNp0YlOn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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train the modified INCEPTIONV3 model that will accept the sagittal data and classify the knee is it has an abnormal tear or not \n",
    "sagittal_abnormal_model = fit_model(training_set_sagittal, training_label_abnormal, \"sagittal_abormal_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 682
    },
    "colab_type": "code",
    "id": "8EQtUg7STjJ2",
    "outputId": "8594dc4e-bb21-4ad7-8a77-5284329053af"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1073 samples, validate on 57 samples\n",
      "Epoch 1/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5274 - acc: 0.7931 - val_loss: 1.0896 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 2/30\n",
      "  32/1073 [..............................] - ETA: 6s - loss: 0.4490 - acc: 0.8438"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4937 - acc: 0.8145 - val_loss: 1.6744 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 3/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4861 - acc: 0.8145 - val_loss: 1.3125 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 4/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.4887 - acc: 0.8145 - val_loss: 1.6482 - val_acc: 0.8421\n",
      "-val_f1 : 0.000000 \n"
     ]
    },
    {
     "data": {
      "image/png": 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tTxn50Ucf4ebmVuYDYaV99dVXpKWloSgKnp6ejBlTfBm6X79+/PjjjxWOnGXK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\nhqGcAQAwDOUMAIBhKGcAAAzzX6utICmB8bXdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train the modified INCEPTIONV3 model that will accept the sagittal data and classify the knee is it has an acl tear or not \n",
    "sagittal_acl_model = fit_model(training_set_sagittal, training_label_acl, \"sagittal_acl_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 736
    },
    "colab_type": "code",
    "id": "gPKd-N7eTjUg",
    "outputId": "ef8e41e9-2799-4f3d-a3d8-7cc03f5c83b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1073 samples, validate on 57 samples\n",
      "Epoch 1/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.6548 - acc: 0.6570 - val_loss: 1.3852 - val_acc: 0.6140\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-val_f1 : 0.000000 \n",
      "Epoch 2/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.6220 - acc: 0.6561 - val_loss: 0.7793 - val_acc: 0.5614\n",
      "-val_f1 : 0.489796 \n",
      "Epoch 3/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.6019 - acc: 0.6570 - val_loss: 1.9022 - val_acc: 0.5965\n",
      "-val_f1 : 0.000000 \n",
      "Epoch 4/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5808 - acc: 0.6841 - val_loss: 1.2882 - val_acc: 0.5789\n",
      "-val_f1 : 0.294118 \n",
      "Epoch 5/30\n",
      "1073/1073 [==============================] - 7s 7ms/step - loss: 0.5752 - acc: 0.6878 - val_loss: 1.8306 - val_acc: 0.5965\n",
      "-val_f1 : 0.000000 \n"
     ]
    },
    {
     "data": {
      "image/png": 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WqEy7qi2VjlPqKlja8WqTsdR08OL0vTOsOLeeLL1O6ViiFESmRPHZqYXcSL5F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\nAObMmYNAIIBwePQhEy9nG0IIIYTIiracvV4vWltbs9N2ux0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r5TqHnMbehUTkPHi1rWCf\nztVSl7GolboMrUc64Ye3c2dV9nFeK8cEKG1dJElCPHgGQXc7EuFOAIBK64TJtVIZkGLi+nCa6GMi\niilE+48i6G5HOuEDAGhNc2B2rYTGVNqbx+j7Nfr+RkI9hJVRLHgWvq7XIaaj0FkXwtF0D42GVKV4\njQ31c7+Z7Va1t+NlOQNCo4NVHUlMI+I/ipB7L1JxDwBAa5oFk2sltKY5NZHRYlkVjHVLYXBch3jw\nNIJuuVOTeOgsVNp6mOtXQm9tpRGxyoiCcxnIaewPEOz7o5LGvgPGuutr4j/9VCanuW+TOy05/yb6\nz78hp7ln3kE9N1UBeWSoQ8rIUGEALPS2a2B2razZrlsZhoHOMg86yzwkIhcRcu9FdOAEfF27MXDp\nPZicN8LoWAa2Bh7hrDYUnCeZkArB27kLiXAXOLUVzllfgVo/rdzFIiWkty6EWtcA77kdiPR/hmT0\nIupmfQUqbXWkuacaeWSofYj0fwZJTIFhNTC5VsojQKnN5S7epNEYpkMz6wGkE1+Sb3rzfYqBS+8h\n0PsRjI7rYHItB6+mEbEmCwXnSRQPfgFv1+sQ0xHoLAvgaPoynZHWKF5jQ/28b8J/8V2EvQfQe+pl\n2GfeBYN9cfGNyaRIRC7IjxkNnAQggVNZYGpcDqPjuin9lASvscI24zZYGtYg7DuEkGd/zohYi+Q+\nwalBMeEoOE8CSRIR6P0Dgr1/ABhWHifYWRs9S5GRMSwP+8w7oDU2w3f+Lfi6diMe7oJtxu2U5i4T\neWSoDoTc7UhEugEAKl0jzK6V0NsWgmHoGmsGy2thrm+DybkCEf8xJeV9HNGB49AYm2F2rYTWPJd+\nxyYIBecJJqTCShq7E5zairqWB2gc1ilGb1sElV5Jc/sOIxnJpLmp3+PJIoopRHxHEPLsRTrRDwDQ\nmufKdycbmynAjIJhORgd18JgX4x46AuE3O2Ih76AJ9wFXlMHszIi1kTevT4V0b/mBIqHzsHbuUtJ\nY89X0tiV/0wkKT2Vxo6GeX8N/8XfIuw9iN5T22CfeTcM9mvKXbSaJqTCCHkPIOw5CFGIAQwHg+M6\nmF0r6B6AcWIYBjrzHOjMc5CM9WUH9+jv/hUGet6HyamMiMXry13UmkDBeQJIkohg70cI9H4IgIV1\n+q0wOZfT2fkUJ6e574TG2Iz+82/B1/U6EuEuWGfcRmnuEkvFPQi69yLS/yd5ZChOB3P9zTA5bwCn\nMhbfARmVWlcPR/NGWBrXIuzZj5DvkPwESu8fYXAsgcm5HCqto9zFrGoUnEtMSIXh63od8dA5cCoL\n6mY9UPa+dkllMdha5bu5O3ci7PsUiehFudMS+jG7IpmRodxn30M8eBoAwKttMLlWwGC/lnptmwC8\n2gzr9PUwN9yMsO8zhDx7EfYeRNh7EDrLfJhdKyHVLSp3MasSBecSioc6lTR2GDrzPNibN1ZF135k\n8qm0DjnNfWEPwr5Dcpq76R4YbK3FNyZ5JElEdOAEQn3tSMZ6AABqwwyYlZGhJnsUpqmI5TQwu5bD\n5LwB0YHPEVIG24gFTiHsboLOvhw6y3w6FuNAwbkEJElCsO8jBHo+BMDAOn0DTM4VlMYmo2JYHvam\nu+Q0d/ev5NHIwl2wTb+Vbq4ZA1FIIOw7jJB7H4RUAABgdV0DjfV6aAw0Al45MAwLg60VeusiJCLn\nEXK3IxLoQCRwXsliLIfBvoSyGGNAvwBXSEhFlDT2F+BUZiWNTT8MZOwM9quh1jfAe24nwt6DSEQu\nyHdza+zlLlpFSieD2U4yJEEZGaruBphcyzFtRnPN9ONczRiGgdbYDK2xGSZ9DF0n30O4/wj8F36D\nQM8HMNZdr1z/pxGxRkLB+QrEw13wde6CkApBa54LR/NGulORXBaVtg718+U0d8T3KXpPvgRH05eh\nt9H1uoxktFcZGeoY5JGhDDA33qSMDEX/7yqV1uCCveluWBrXynfOew8i2PfHnBGxVtCIWAVQcL4M\nchr7YwR63gcAWKeth8m1ktLY5IqwrAqOpruhVdLc3s4dMIZvgG36himb5pYkCfHQWeXZ2nMAAF5b\nB7NrJQy2a6bsv0s14lQGWBtvgbm+DZH+I/KjWP2fIdL/GbTmq5Rnzlvod1RB3+xxEtJR+DpfRzx0\nFpzKJHcqYmwqd7FIDTHYr4Fa3wjvuR0Iew8gqaS5eY2t3EWbNPLIUMeUkaHcAACNsUXpleoq+gGv\nYiyrgqnuehgdy5Te2j5BPHgG8eAZqHQNSm9ti6Z8b20UnMchET4Pb+dOJY19FRzN91I6jUwIOc29\nGf4Lv0HEdxg9J1+Co/nL0FsXlrtoE0pMxxDyHkLYsx9COgyAgd52tTIyVGPR7Un1YBgGeut86K3z\nkYhcULoH/Ry+rteVEbGWw1h33ZQdRpeC8xhIkoSQ+xMMXPo9AMDSuA7m+jY6eycTSk5z3wONoQn+\nC7+G99wvYXTeCNu09TWXzk0n/Ah69iHiO6yMDKWGybVCGRnKUu7ikQmmMcyAZtZX8r4HA5feRaD3\nQxjrlk7J70Ft/Q+fAEI6Cl/XG4gHT4NTmeBouR9aY3O5i0WmEKPjWmj00+Dt3IGwZ7+c5m55oCbS\n3PIYwu2IDnwOeWQoM0wNa2CsWzplW0xTGa+xwT7jdnlELCWDEnLvRci9D3pb65TKoFBwHkUi3K2k\nsYPQmubIaWyVodzFIlOQSudE/bzN8F94B5H+I+g5tU2+m9u6oNxFGzdJknJGhjoPAHStkeTheB0s\nDatgdmVGxGpH1H8MUf+xKXPvAQXnAuQ0druSxpZgaVwLc/2qmv4ikMrHcmo4mjdCY2yGv/vX8J77\nH5icy2Gdth4MW/kBTRRTiPT/CSH3XqQTPgBQ7tJdAY1xFv3/IsMwLA+jYwkM9mvz7tr3hDuh0jrl\nrllr9K792qvRFRLSMfR3vYFYsAMcb5TT2KaWcheLkCyjYwnU+mnwntuBkGef0mnJA+DV1nIXrSAh\nFck+3yqmo/LIUPYl9HwrGTN5RKyroDNflfe8e//5tzBw6fcwOW+suefdKTjnSEQuwHtuJ4RUAFrT\nbDia76M0NqlIap0LDfO/hf7uXyPq/xN6T74Ee/NG6C3zy120rFTcqzzL+idIUhosp4W5fhX1DEWu\niFrfgLqWe5Getk7uKc77KQI97yPY90flpG95TfSuR8EZShrbsw8DF38HOY19i5LGpk7aSeXKpLm1\npmb4u9+B94vtMLlWwDrtS2W7bitJUrZP5VigAwDAqa0wu1ZQn8qkpHi1GbbpG2BpWJ3tYz3sPYCw\n9wB01oXy5ZIq7kp5ygdnMR2D7/ybiAVOgeUNqGu5H1rTrHIXi5AxYRgGRsd1g2lu914kwt1ypyWT\n+OiJPDKUPBpRMnoJAKDWT4fZtRI66wI60SUTRh4Ra4U8Ipb/cwTd7YgNfI7YwOfK6GQrq3JErCkd\nnBORi/B27oCQDEBjbEFdy/00EDupSmpdvZLmfhtR/zG5b+7mjdBZ5k3o35VHhvoMIc8+CMkBAMiO\n46s2zKSbvMikYRgOBvvV0NtakQh3IehuRzx4Gt5zvxwc19uxBCyrKndRx2RKBmc5jb0fA5feBSQR\nloY1MDfcXHVnVoTkYjkNHM33QWtsQf+Fd+D54jWYXDfBOm1tydPc6VRIfgbVewiSEAfD8MpIQ8uh\n0jpK+rcIGQ+GYaA1tUBrakEq7kFQue/Bf+GdISNiVXZDbMoFZzEdV9LYJ5U09n3QmmaXu1iElATD\nMDDWLZXT3J07EHJ/gmSkG46W+0uS5k7G+uSbvPxHAUkEy+thbrwFxrrra+pOWVIbVFonHE33wJoZ\nEctzEMG+jxB0fwKDfTHMrhVQaZ3lLmZBUyo4J6KX4D23A0JyABpjs5LGprtGSe1R6xvQMP9h9J//\nFaIDx5U0973QWeaOe1/yyFBfKM+YfgEA4DV1MLtWQG+/pmrShGTq4lRGWBvXwuxSRsRSugiN+A5D\na56rjIjVXFGXYaZEcJYkCWHvAfgvvgtIAswNN8PSsIbS2KSmsZwGjpb7ofE1w39hDzxf/D+Y69tg\naVw7pu++JAo5I0P1AQA0xmald6a5FfVDRshYsJwaJucNMNbJI2IF3Z8gHjyNePA0VLpGpZe6hRXR\nS13NB2dRiMN3/i3EBj4Hy+vhaL4POvOccheLkEnBMAxMdddDo58O77kdCPZ9jEQ4k+Y2F9xGTMcR\n9h1CyLMfQioEgIHe2gpT/Upo9NMmtwKETACGYaG3LoDeugCJSDeC7r2IDXwOX9cuDFyywOS6EUbH\nUrCcpmxlHFNwfvbZZ3HkyBEwDIMtW7Zg8eLF2WWffPIJnn/+eXAch9WrV+Pxxx8vus1kSUZ74D23\nA+mkHxpjExwtD4CnNDaZgtT6RjQseAT9599CdOAEek8paW7zVdl10okBuVMH32FIYlIeGcq5XB4R\nSFOZvY8RcqU0hplwzpqJVKIfIfc+RPo/w8DFdxHo+UPOiFiFT2QnUtHgvH//fnR1dWH79u04e/Ys\ntmzZgu3bt2eX/9M//RNeeeUV1NfX46GHHsJtt92G/v7+UbeZaJIkwX3+E/R2vCmnsetXwdJ4C6Wx\nyZQmp7kfgMbbDP/F38Jz9v/CXL8KetV18J77PaIDJyCPDGWCqeFmGB3LwPI0MhSZGlQaO+wz74Cl\nUR4RK+TZj5C7PW9ELGDyGndFg3N7ezvWr18PAJgzZw4CgQDC4TCMRiO6u7thsVjQ2CgP4bVmzRq0\nt7ejv79/xG0mQ6D3AwR7P1LS2PmtA0KmMoZhYHLeAI1hOrzndiLY90cE+/4IAFBp62GuXwm9tbUq\nBtIgZCJwvB6Whpthdq1ExH8UQXc7ov6jiPqPAsm1UFtvnpRyFA3OXq8Xra2t2Wm73Q6PxwOj0QiP\nxwO73Z63rLu7G36/f8RtRmKz6cHzpflBkGJ2sOLVmLlgI9Ta2kjHOZ21k46vlbpUdz3mo2Ha3+L8\nyTcgpKJwNa+CyV4bN3lV93EZVCv1AKq4LvWrIc1fhaD3FDzd7eB47aTVZdw3hEmSNO4/MpZt/P7o\nuPc7EkZ3LeYsWQWPJwSEQiXbb7k4nSa5LjWgVupSK/UwNtyVrUvCGy53ca5YrRyXWqkHUCt1mQHL\nzAdLXpfRAn3R4OxyueD1erPTbrcbTqez4LK+vj64XC6oVKoRtyGEEELI6IreIdXW1oY9e/YAAI4f\nPw6Xy5VNT8+YMQPhcBgXLlxAOp3G+++/j7a2tlG3IYQQQsjoiracly5ditbWVmzatAkMw2Dr1q3Y\ntWsXTCYTNmzYgB/84Ad46qmnAAB33nknZs2ahVmzZg3bhhBCCCFjw0iXcxF5ApT6mkRtXOeQUV0q\nT63UA6C6VKJaqQdAdSm2v5HQg7+EEEJIhaHgTAghhFQYCs6EEEJIhaHgTAghhFQYCs6EEEJIhaHg\nTAghhFQYCs6EEEJIhaHgTAghhFQYCs6EEEJIhamYHsIIIYQQIqOWMyGEEFJhKDgTQgghFYaCMyGE\nEFJhKDgTQgghFYaCMyGEEFJhKDgTQgghFYYvdwGu1LPPPosjR46AYRhs2bIFixcvzi775JNP8Pzz\nz4PjOKxevRqPP/54GUta3Gh1WbduHRoaGsBxHADgueeeQ319fbmKWlRHRwcee+wxfOMb38BDDz2U\nt6zajstodam24/LjH/8Yhw4dQjqdxqOPPopbb701u6yajsto9aimYxKLxfB3f/d38Pl8SCQSeOyx\nx7B27drs8mo6JsXqUk3HBQDi8TjuvvtuPPbYY7j//vuz8yftmEhVbN++fdIjjzwiSZIknTlzRvrq\nV7+at/yOO+6QLl26JAmCIH3ta1+TTp8+XY5ijkmxuqxdu1YKh8PlKNq4RSIR6aGHHpL+4R/+QfrF\nL34xbHk1HZdidamm49Le3i5961vfkiRJkvr7+6U1a9bkLa+W41KsHtV0TN5++23ppZdekiRJki5c\nuCDdeuutecur5ZhIUvG6VNNxkSRJev7556X7779f2rlzZ978yTomVZ3Wbm9vx/r16wEAc+bMQSAQ\nQDgcBgB0d3fDYrGgsbERLMtizZo1aG9vL2dxRzVaXaqNWq3Gtm3b4HK5hi2rtuMyWl2qzQ033ICf\n/OQnAACz2YxYLAZBEABU13EZrR7V5s4778TDDz8MAOjp6clrSVbTMQFGr0u1OXv2LM6cOYNbbrkl\nb/5kHpOqTmt7vV60trZmp+12OzweD4xGIzweD+x2e96y7u7uchRzTEarS8bWrVtx8eJFLFu2DE89\n9RQYhilHUYvieR48X/irVW3HZbS6ZFTLceE4Dnq9HgCwY8cOrF69OptirKbjMlo9MqrlmGRs2rQJ\nvb29+OlPf5qdV03HJFehumRUy3H50Y9+hGeeeQa7d+/Omz+Zx6Sqg/NQUg31RDq0Lk8++SRuvvlm\nWCwWPP7449izZw9uv/32MpWOZFTjcfnd736HHTt24NVXXy13Ua7ISPWoxmPy2muv4fPPP8f3v/99\nvPnmmxUbtMZipLpUy3HZvXs3lixZgpkzZ5a1HFWd1na5XPB6vdlpt9sNp9NZcFlfX19FpyZHqwsA\n3HvvvXA4HOB5HqtXr0ZHR0c5innFqu24FFNtx+Wjjz7CT3/6U2zbtg0mkyk7v9qOy0j1AKrrmBw7\ndgw9PT0AgIULF0IQBPT39wOovmMyWl2A6jkuH3zwAd577z189atfxS9/+Uv8+7//Oz755BMAk3tM\nqjo4t7W1Yc+ePQCA48ePw+VyZdPAM2bMQDgcxoULF5BOp/H++++jra2tnMUd1Wh1CYVC2Lx5M5LJ\nJADgwIEDmDt3btnKeiWq7biMptqOSygUwo9//GO8+OKLsFqtecuq6biMVo9qOyYHDx7Mtvy9Xi+i\n0ShsNhuA6jomwOh1qabj8i//8i/YuXMn/ud//gcPPvggHnvsMdx0000AJveYVP2oVM899xwOHjwI\nhmGwdetWnDhxAiaTCRs2bMCBAwfw3HPPAQBuvfVWbN68ucylHd1odfnZz36G3bt3Q6PRYNGiRXjm\nmWcqNvV17Ngx/OhHP8LFixfB8zzq6+uxbt06zJgxo+qOS7G6VNNx2b59O1544QXMmjUrO2/58uWY\nP39+VR2XYvWopmMSj8fx93//9+jp6UE8HscTTzyBgYGBqvwNK1aXajouGS+88AKmT58OAJN+TKo+\nOBNCCCG1pqrT2oQQQkgtouBMCCGEVBgKzoQQQkiFoeBMCCGEVBgKzoQQQkiFoeBMCCGEVBgKzoQQ\nQkiFoeBMCCGEVJj/DwNXp9Ix5lN3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train the modified INCEPTIONV3 model that will accept the sagittal data and classify the knee is it has a meniscus tear or not \n",
    "sagittal_meniscus_model = fit_model(training_set_sagittal, training_label_meniscus, \"sagittal_meniscus_model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T2OJcQKsZbs4"
   },
   "source": [
    "## Evaluate the 9 models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rLaW7IDwZeXD"
   },
   "outputs": [],
   "source": [
    "#load the test data of one single angle of the exams (axial | coronal | sagittal) by giving the angle name\n",
    "def testSet(file_name):\n",
    "    test_set = []\n",
    "    for np_name in sorted(glob.glob('MRNet-v1.0/valid/'+file_name+'/*.npy')):\n",
    "        test_set.append(np.load(np_name))\n",
    "    return test_set   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Zokr_v_0aXCD"
   },
   "outputs": [],
   "source": [
    "#load the test data for each angle of the knee\n",
    "test_set_axial= testSet('axial')\n",
    "test_set_coronal=testSet('coronal')\n",
    "test_set_sagittal=testSet('sagittal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ewlW0t1-atSY"
   },
   "outputs": [],
   "source": [
    "#extract the middel 3 slices from each stack\n",
    "test_set_axial = extract_3(test_set_axial, 120)\n",
    "test_set_coronal =extract_3(test_set_coronal, 120)\n",
    "test_set_sagittal =extract_3(test_set_sagittal, 120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6U0JME9GbQ6c"
   },
   "outputs": [],
   "source": [
    "#load the labels of the test data for each knee tear \n",
    "test_label_abnormal = load_labels('MRNet-v1.0/valid-abnormal.csv')\n",
    "test_label_acl = load_labels('MRNet-v1.0/valid-acl.csv')\n",
    "test_label_meniscus = load_labels('MRNet-v1.0/valid-meniscus.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "9s9dhoElbugv",
    "outputId": "c7fc4845-a830-46bc-c4f8-0971412b9ff3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120/120 [==============================] - 36s 296ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[2.620534899534444, 0.7916666666666666]"
      ]
     },
     "execution_count": 67,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axial_abnormal_model.evaluate(test_set_axial, test_label_abnormal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "RcjfITHubwXY",
    "outputId": "49b4abf1-a35f-4723-f7a7-e0fa1b1e6dc1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120/120 [==============================] - 36s 298ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[4.1630372505635025, 0.55]"
      ]
     },
     "execution_count": 68,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axial_acl_model.evaluate(test_set_axial, test_label_acl) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "uqxIfVvUbwtx",
    "outputId": "d300a141-49ca-4eb5-d216-69396e97a2ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120/120 [==============================] - 35s 294ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.2801123062769573, 0.5500000002483527]"
      ]
     },
     "execution_count": 69,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axial_meniscus_model.evaluate(test_set_axial, test_label_meniscus) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "gEgu3jd4bw3W",
    "outputId": "22db23f1-8704-458a-9857-a0fb3eb56106"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120/120 [==============================] - 35s 295ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.7501238999771886, 0.7916666666666666]"
      ]
     },
     "execution_count": 70,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coronal_abnormal_model.evaluate(test_set_coronal, test_label_abnormal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "ZPQpGpC2bw7v",
    "outputId": "3b476a38-2ccf-43b9-f9e1-16af05a64635"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120/120 [==============================] - 36s 296ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[3.530718553562959, 0.55]"
      ]
     },
     "execution_count": 71,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coronal_acl_model.evaluate(test_set_coronal, test_label_acl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "FmvpiJsnbw-r",
    "outputId": "1aeb6df0-9e1c-4280-f03e-fa59dc0b64a4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120/120 [==============================] - 36s 297ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.8160638789335886, 0.5666666666666667]"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coronal_meniscus_model.evaluate(test_set_coronal, test_label_meniscus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "HN2eLE6ObxBM",
    "outputId": "90b83165-53ab-45c1-b395-5875d3b38d40"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120/120 [==============================] - 36s 302ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.6186912363317485, 0.7916666666666666]"
      ]
     },
     "execution_count": 73,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sagittal_abnormal_model.evaluate(test_set_sagittal, test_label_abnormal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "mqIhEuzrsqDE",
    "outputId": "5f8dc3f7-192d-4fc4-843a-627de4cff489"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120/120 [==============================] - 37s 305ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[2.8889908686280252, 0.55]"
      ]
     },
     "execution_count": 74,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sagittal_acl_model.evaluate(test_set_sagittal, test_label_acl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "ZTcUHNtEeVOQ",
    "outputId": "39000f16-7da8-4bb0-d9ee-478d7ed744eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120/120 [==============================] - 37s 305ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.4990095178286234, 0.5666666666666667]"
      ]
     },
     "execution_count": 75,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sagittal_meniscus_model.evaluate(test_set_sagittal, test_label_meniscus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s4FM2wYUeZYm"
   },
   "outputs": [],
   "source": [
    "#predict if the knee has an acl tear by using the data of the 3 knee angel (axial, coronal & sagittal)\n",
    "#and the 3 models that accept one of these data and predict if the knee has an abnromal tear or not\n",
    "#by doing a majority voting between the 3 models\n",
    "def predict_abnormal(axial_input, coronal_input, sagittal_input, ground_truth):\n",
    "    axial_abnormal_prediction = axial_abnormal_model.predict(axial_input)\n",
    "    coronal_abnormal_prediction = coronal_abnormal_model.predict(coronal_input)\n",
    "    sagittal_abnormal_prediction = sagittal_abnormal_model.predict(sagittal_input)\n",
    "    \n",
    "    #to say that the exam has the acl tear or not, each of the models will vote in this decision\n",
    "    #and the final decision will be made by taking the max vote\n",
    "    predict_voting = []\n",
    "    for i in range (len(axial_abnormal_prediction)):\n",
    "        voting_list =[0, 0] \n",
    "        \n",
    "        #make the axial_acl_model vote for the final decision \n",
    "        if(axial_abnormal_prediction[i]>0.5):\n",
    "            voting_list[1] +=1\n",
    "        else:\n",
    "            voting_list[0] += 1\n",
    "    \n",
    "        #make the coronal_acl_model vote for the final decision \n",
    "        if(coronal_abnormal_prediction[i]>0.5):\n",
    "            voting_list[1] +=1\n",
    "        else:\n",
    "            voting_list[0] += 1\n",
    "            \n",
    "        #make the sagittal_acl_model vote for the final decision \n",
    "        if(sagittal_abnormal_prediction[i]>0.5):\n",
    "            voting_list[1] +=1\n",
    "        else:\n",
    "            voting_list[0] += 1\n",
    "        \n",
    "        #give the exam the prediction that has the most votes\n",
    "        predict_voting.append(np.argmax(voting_list))\n",
    "    \n",
    "  \n",
    "    return accuracy_score(ground_truth, np.array(predict_voting)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "41dMCBZleZgS"
   },
   "outputs": [],
   "source": [
    "#predict if the knee has an acl tear by using the data of the 3 knee angel (axial, coronal & sagittal)\n",
    "#and the 3 models that accept one of these data and predict if the knee has an acl tear or not\n",
    "#by doing a majority voting between the 3 models\n",
    "def predict_acl(axial_input, coronal_input, sagittal_input, ground_truth):\n",
    "    axial_acl_prediction = axial_acl_model.predict(axial_input)\n",
    "    coronal_acl_prediction = coronal_acl_model.predict(coronal_input)\n",
    "    sagittal_acl_prediction = sagittal_acl_model.predict(sagittal_input)\n",
    "    \n",
    "    #to say that the exam has the acl tear or not, each of the models will vote in this decision\n",
    "    #and the final decision will be made by taking the max vote\n",
    "    predict_voting = []\n",
    "    for i in range (len(axial_acl_prediction)):\n",
    "        voting_list =[0, 0] \n",
    "        \n",
    "        #make the axial_acl_model vote for the final decision \n",
    "        if(axial_acl_prediction[i]>0.5):\n",
    "            voting_list[1] +=1\n",
    "        else:\n",
    "            voting_list[0] += 1\n",
    "    \n",
    "        #make the coronal_acl_model vote for the final decision \n",
    "        if(coronal_acl_prediction[i]>0.5):\n",
    "            voting_list[1] +=1\n",
    "        else:\n",
    "            voting_list[0] += 1\n",
    "            \n",
    "        #make the sagittal_acl_model vote for the final decision \n",
    "        if(sagittal_acl_prediction[i]>0.5):\n",
    "            voting_list[1] +=1\n",
    "        else:\n",
    "            voting_list[0] += 1\n",
    "        \n",
    "        #give the exam the prediction that has the most votes\n",
    "        predict_voting.append(np.argmax(voting_list))\n",
    "    \n",
    "  \n",
    "    return accuracy_score(ground_truth, np.array(predict_voting)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "H_kbSJWyeZkB"
   },
   "outputs": [],
   "source": [
    "#predict if the knee has an meniscus tear by using the data of the 3 knee angel (axial, coronal & sagittal)\n",
    "#and the 3 models that accept one of these data and predict if the knee has an meniscus tear or not\n",
    "#by doing a majority voting between the 3 models\n",
    "def predict_meniscus(axial_input, coronal_input, sagittal_input, ground_truth):\n",
    "    axial_meniscus_prediction = axial_meniscus_model.predict(axial_input)\n",
    "    coronal_meniscus_prediction = coronal_meniscus_model.predict(coronal_input)\n",
    "    sagittal_meniscus_prediction = sagittal_meniscus_model.predict(sagittal_input)\n",
    "    \n",
    "    #to say that the exam has the meniscus tear or not, each of the models will vote in this decision\n",
    "    #and the final decision will be made by taking the max vote\n",
    "    predict_voting = []\n",
    "    for i in range (len(axial_meniscus_prediction)):\n",
    "        voting_list =[0, 0] \n",
    "        \n",
    "        #make the axial_meniscus_model vote for the final decision \n",
    "        if(axial_meniscus_prediction[i]>0.5):\n",
    "            voting_list[1] +=1\n",
    "        else:\n",
    "            voting_list[0] += 1\n",
    "    \n",
    "        #make the coronal_meniscus_model vote for the final decision \n",
    "        if(coronal_meniscus_prediction[i]>0.5):\n",
    "            voting_list[1] +=1\n",
    "        else:\n",
    "            voting_list[0] += 1\n",
    "            \n",
    "        #make the sagittal_meniscus_model vote for the final decision \n",
    "        if(sagittal_meniscus_prediction[i]>0.5):\n",
    "            voting_list[1] +=1\n",
    "        else:\n",
    "            voting_list[0] += 1\n",
    "        \n",
    "        #give the exam the prediction that has the most votes\n",
    "        predict_voting.append(np.argmax(voting_list))\n",
    "    \n",
    "  \n",
    "    return accuracy_score(ground_truth, np.array(predict_voting)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "ZFK0To77elJL",
    "outputId": "19da360f-b219-48f8-ecf2-d09b5b039d22"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79.16666666666666\n"
     ]
    }
   ],
   "source": [
    "#evaluate the abnormal ensemble models\n",
    "print (predict_abnormal(test_set_axial, test_set_coronal, test_set_sagittal, test_label_abnormal))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "sK1HlpfmelMb",
    "outputId": "068f990b-c325-467c-df1a-d4d08b83efdd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.00000000000001\n"
     ]
    }
   ],
   "source": [
    "#evaluate the acl ensemble models\n",
    "print (predict_acl(test_set_axial, test_set_coronal, test_set_sagittal, test_label_acl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "HIxG9mqielPy",
    "outputId": "a9b781cf-1ec9-4b22-93eb-6b8db9f0519f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.666666666666664\n"
     ]
    }
   ],
   "source": [
    "#evaluate the meniscus ensemble models\n",
    "print (predict_meniscus(test_set_axial, test_set_coronal, test_set_sagittal, test_label_meniscus))"
   ]
  }
 ],
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