main.py

"""
Skin cancer lesion classification using the HAM10000 dataset

Dataset link:
https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000
Data description: 
    https://arxiv.org/ftp/arxiv/papers/1803/1803.10417.pdf

The 7 classes of skin cancer lesions included in this dataset are:
Melanocytic nevi (nv)
Melanoma (mel)
Benign keratosis-like lesions (bkl)
Basal cell carcinoma (bcc) 
Actinic keratoses (akiec)
Vascular lesions (vas)
Dermatofibroma (df)
"""


import numpy as np
import pandas as pd
import os

from glob import glob
from PIL import Image

np.random.seed(42)
from sklearn.metrics import confusion_matrix

from tensorflow.keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder

skin_df = pd.read_csv('data/HAM10000_metadata.csv')

SIZE=32

le = LabelEncoder()
le.fit(skin_df['dx'])
LabelEncoder()
 
skin_df['label'] = le.transform(skin_df["dx"]) 


from sklearn.utils import resample

df_0 = skin_df[skin_df['label'] == 0]
df_1 = skin_df[skin_df['label'] == 1]
df_2 = skin_df[skin_df['label'] == 2]
df_3 = skin_df[skin_df['label'] == 3]
df_4 = skin_df[skin_df['label'] == 4]
df_5 = skin_df[skin_df['label'] == 5]
df_6 = skin_df[skin_df['label'] == 6]

n_samples=500 
df_0_balanced = resample(df_0, replace=True, n_samples=n_samples, random_state=42) 
df_1_balanced = resample(df_1, replace=True, n_samples=n_samples, random_state=42) 
df_2_balanced = resample(df_2, replace=True, n_samples=n_samples, random_state=42)
df_3_balanced = resample(df_3, replace=True, n_samples=n_samples, random_state=42)
df_4_balanced = resample(df_4, replace=True, n_samples=n_samples, random_state=42)
df_5_balanced = resample(df_5, replace=True, n_samples=n_samples, random_state=42)
df_6_balanced = resample(df_6, replace=True, n_samples=n_samples, random_state=42)

skin_df_balanced = pd.concat([df_0_balanced, df_1_balanced, 
                              df_2_balanced, df_3_balanced, 
                              df_4_balanced, df_5_balanced, df_6_balanced])

image_path = {os.path.splitext(os.path.basename(x))[0]: x
                     for x in glob(os.path.join('./data/images', '*.jpg'))}

skin_df_balanced['path'] = skin_df['image_id'].map(image_path.get)
print("Loading data...")

skin_df_balanced['image'] = skin_df_balanced['path'].map(lambda x: np.asarray(Image.open(x).resize((SIZE,SIZE))))



X = np.asarray(skin_df_balanced['image'].tolist())
X = X/255. 
Y=skin_df_balanced['label'] 
Y_cat = to_categorical(Y, num_classes=7)
x_train, x_test, y_train, y_test = train_test_split(X, Y_cat, test_size=0.25, random_state=42)


num_classes = 7

model = Sequential()
model.add(Conv2D(256, (3, 3), activation="relu", input_shape=(SIZE, SIZE, 3)))

model.add(MaxPool2D(pool_size=(2, 2)))  
model.add(Dropout(0.3))

model.add(Conv2D(128, (3, 3),activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))  
model.add(Dropout(0.3))

model.add(Conv2D(64, (3, 3),activation='relu'))
#model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2, 2)))  
model.add(Dropout(0.3))
model.add(Flatten())

model.add(Dense(32))
model.add(Dense(7, activation='softmax'))
model.summary()

model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['acc'])

batch_size = 16 
epochs = 50

history = model.fit(
    x_train, y_train,
    epochs=epochs,
    batch_size = batch_size,
    validation_data=(x_test, y_test),
    verbose=2)

score = model.evaluate(x_test, y_test)
print('Test accuracy:', score[1])


loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(loss) + 1)



acc = history.history['acc']
val_acc = history.history['val_acc']

y_pred = model.predict(x_test)

model.save('model.h5')
print(y_pred)