Human Fracture Detection Using Machine Learning
B. Kalyani · International Journal for Research in Applied Science and Engineering Technology · 2024
Abstract: Accurate and timely detection of fractures is crucial for effective medical diagnosis and treatment planning. In this project, we propose a novel approach for human fracture detection by leveraging the YOLO (You Only Look Once) model, known for its efficient object detection capabilities. Our system aims to automatically identify and localize fractured areas within X-ray images by highlighting them with bounding boxes, facilitating prompt diagnosis. Utilizing deep learning techniques, especially the efficient object detection capabilities of YOLO, we enable rapid and precise localization of fractures, assisting radiologists and healthcare professionals in promptly diagnosing fractures. The proposed system offers a promising solution to enhance the efficiency and accuracy of fracture detection, ultimately improving patient care and treatment outcomes significantly.