Next-Gen Fracture Diagnosis: Integrating YOLOv9 for Rapid Detection and Treatment

This research study proposes a web-based application that integrates YOLOv9, a state-of-the-art object detection model, with deep learning and computer vision technologies to detect fractures in medical images such as X-rays. The system supports healthcare providers by automating the detection of fractures and offering real-time treatment suggestions based on the severity of the injury, enhancing diagnosis accuracy and treatment planning efficiency. For minor fractures, the system provides home treatment recommendations, while for severe cases, it suggests nearby hospitals using the patient's location to ensure timely medical attention. The application's user-friendly interface, built using Flask, allows easy access for both healthcare professionals and patients, streamlining the medical diagnosis process. By leveraging cutting-edge deep learning and object detection technologies, this project presents a novel AI-driven solution that has the potential to significantly improve medical imaging and diagnostic practices.