Edge AI-based Bone Fracture Detection using TFlite

Bone fractures are among the most common injuries requiring prompt and accurate diagnosis to ensure effective treatment. Traditional manual interpretation of X-ray images is often time-consuming and prone to human error, highlighting the need for automated solutions. This project presents an Edge AI-based system designed for detecting and classifying bone fractures using TensorFlow Lite. By leveraging lightweight deep learning models such as Convolutional Neural Networks (CNN) and MobileNet, and introducing a hybrid approach combining MobileNet with a Random Forest classifier, the system achieves high accuracy while maintaining computational efficiency. Developed using Python, the solution is optimized for real-time diagnosis on portable devices, making it highly accessible in both clinical and remote healthcare settings. Experimental results demonstrate improved fracture detection accuracy, supporting the potential of Edge AI to enhance diagnostic reliability and reduce the workload on medical professionals.