Bone Fracture Detection System using Machine Learning

Bone fractures are a prevalent medical issue, requiring timely and accurate diagnosis for effective treatment and recovery. Traditional fracture detection relies heavily on the manual interpretation of X-rays and other imaging modalities by radiologists, a process that can be time-consuming and prone to human error, especially in busy clinical settings. The increasing volume of medical images generated in modern healthcare, combined with the global shortage of trained radiologists, highlights the need for an automated and efficient fracture detection solution. This project aims to develop a Bone Fracture Detection System using Artificial Intelligence (AI) and Machine Learning (ML) techniques, particularly deep learning models such as Convolutional Neural Networks (CNNs). The system is designed to automatically detect and classify bone fractures from medical images, providing healthcare professionals with real-time diagnostic support. By training on large datasets of labeled X-ray and CT scan images, the model learns to identify various types of fractures with high accuracy, including subtle and complex fractures that may be missed by the human eye. The system is expected to improve the speed and accuracy of fracture diagnosis, reduce the workload on radiologists, and enhance patient outcomes through earlier intervention. Additionally, it can be applied in telemedicine settings, making high-quality fracture detection accessible to remote and underserved areas. This project demonstrates the potential of AI-driven technologies to revolutionize medical diagnostics and contribute to more efficient and effective healthcare delivery.