DETECTION OF HAND BONE FRACTURE IN X RAY IMAGES USING HYBRID YOLO

In the detection of bone fractures from X-ray images, accurate and timely diagnosis is crucial to prevent further complications and ensure proper healing. Existing models, such as YOLO NAS (You Only Look Once - Neural Architecture Search), have shown potential in object detection but have limitations in detecting subtle bone fractures, particularly small or hairline fractures. To address these shortcomings, the proposed model utilizes YOLO V8, an advanced version of the YOLO framework, which builds on previous models by offering improved accuracy, speed, and efficiency in real-time object detection tasks. YOLO V8 enhances the detection capabilities by refining the architecture and optimizing performance, making it better suited for medical image analysis. The model is trained on a comprehensive dataset of 1200 hand-bone X-ray images, classified into six distinct fracture categories. A comparison of the YOLO V8 model with YOLO NAS highlights the improved ability of V8 to detect complex and subtle fractures, ensuring faster and more reliable diagnoses. This advancement is essential for clinical settings, where delays or misdiagnoses could lead to severe outcomes for patients.