Fracturespot: YOLO-Powered X-Ray Detection System

Bone fracture detection is a crucial aspect of early cancer diagnosis, and deep learning-based object detection models have shown promising results in medical imaging. This study explores the application of YOLO (You Only Look Once) models for fast and reliable bone fracture detection. The dataset used in this research is the Bone Fracture Detection dataset from Roboflow, which includes annotated medical images to train and evaluate the models. Four YOLO variants—YOLOv5x6, YOLOv5s6, YOLOv8n, and YOLOv9n—are implemented and compared based on their efficiency and ability to detect fractures accurately. Performance evaluation metrics such as precision, recall, and mean Average Precision (mAP) are used to assess model effectiveness. Among the tested models, YOLOv8n achieved the highest precision of 90.9%, demonstrating its superior capability in detecting bone fractures accurately. These models are optimized for real-time medical image analysis, ensuring quick and precise fracture identification. The study aims to identify the most effective YOLO variant for detecting bone fractures, balancing speed and reliability. The results demonstrate that advanced YOLO architectures significantly improve early fracture detection, aiding in timely diagnosis and treatment planning. This research contributes to the biomedical field by enhancing automated fracture detection methods, potentially reducing human error and improving healthcare outcomes.