Project Overview Bone Fracture Classification Using Multiple CNN Architectures is a deep learning-based project aimed at identifying fractures in bone X-ray images. The project leverages Convolutional Neural Networks (CNNs) and pre-trained models like ResNet50, InceptionV3, and MobileNetV2 to classify images as fractured or not fractured. The dataset is preprocessed and augmented for training, ensuring improved accuracy and generalization.
Key Features
- Utilization of multiple CNN architectures including custom CNN, ResNet50, InceptionV3, and MobileNetV2.
- Image preprocessing and augmentation to enhance model robustness.
- Training, validation, and testing dataset split for model evaluation.
- Implementation of early stopping and model checkpointing for optimized training.
- Performance evaluation using accuracy metrics and confusion matrices.
- Visualization of classification results and training progress.
Purpose The primary purpose of this project is to develop an automated system that assists medical professionals in detecting bone fractures efficiently. By leveraging deep learning techniques, the model aims to reduce diagnostic errors, speed up the classification process, and provide a reliable tool for medical imaging analysis.
Application
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Medical Imaging: Automated detection of bone fractures in X-ray images.
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Healthcare Assistance: Supporting radiologists and doctors in early fracture diagnosis.
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Research and Development: Advancing AI-driven diagnostic tools in medical fields.
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Telemedicine: Enabling remote analysis of bone fractures through AI-powered systems.
To set up the project locally, follow the steps below:
- Clone the repository:
git clone https://github.com/BhaveshBhakta/Bone-Fracture-Classification-Using-MobileNetV2-InceptionV3-ResNet50.git - Navigate to the project directory:
cd Bone-Fracture-Classification-Using-MobileNetV2-InceptionV3-ResNet50 - Run the jupyter notebook
Contributions are welcome! Feel free to fork the repository, make improvements, and submit a pull request.