AI Driven Detection of Bone Fractures in Medical Imaging

In today’s world, bone fractures are increasingly common due to accidents, sports injuries, stunts and age weakness-related conditions. There are some traditional methods that are used for bone fracture detection, such as X-ray analysis by radiologists, which can be time-consuming and prone to human error. As this project focuses on bone fracture detection using advanced deep learning models, YOLOv8 and R-CNN, both are coupled with image processing techniques to enhance efficiency and accuracy. It provides an easy- to-use interface for users to upload X-ray images along with their names, and receive predictions on whether the image contains a bone fracture or not. YOLOv8 is fast and accurate for spotting objects, whereas Faster R-CNN is great for detailed object spotting. By integrating these technologies, this approach assists healthcare professionals in faster decision-making and minimizing diagnosis time. Using deep learning algorithms, the proposed system aims to provide a faster, more reliable, and cost-effective solution for fracture diagnosis.