Bone Fracture Detection Using Deep Learning

This project implements a convolutional neural network (CNN) to detect bone fractures in X-ray images. The model classifies X-ray images into two categories: fractured and not fractured bones.

Overview

The project uses a custom CNN architecture implemented in TensorFlow/Keras to classify bone X-ray images. It includes:

• Data loading and preprocessing
• Data augmentation
• Model architecture design and training
• Model evaluation and visualization
• Testing and performance metrics

Requirements

• Python 3.8+
• TensorFlow
• Keras
• OpenCV
• NumPy
• Pandas
• Matplotlib
• Seaborn
• scikit-learn
• kagglehub

Dataset

The dataset is sourced from Kaggle's "Bone Fracture" dataset containing X-ray images split into:

• Training set
• Validation set
• Test set

Each image is labeled as either fractured or not fractured.

Model Architecture

The CNN model consists of:

• Multiple convolutional layers with ReLU activation
• Batch normalization layers
• Max pooling layers
• Dropout layers for regularization
• Dense layers
• Final sigmoid activation for binary classification

Performance Metrics

The model is evaluated using:

• Accuracy
• Loss
• Sensitivity
• Specificity
• AUC-ROC
• Confusion Matrix
• Classification Report

Usage

1. Install required dependencies
2. Download the dataset using kagglehub
3. Run the Jupyter notebook to:
   • Load and preprocess data
   • Train the model
   • Evaluate performance
   • Make predictions

Files

• Bone_Fracture.ipynb: Main Jupyter notebook containing the implementation

Results

The model achieves:

• High accuracy in classifying bone fractures
• Good sensitivity and specificity metrics
• Reliable performance on test set predictions

Author

Kwaku Amo-Korankye
10211100331
Academic City