Fracture Detection

Fracture detection research focuses on developing automated methods for identifying fractures in medical images, primarily X-rays and CT scans, to aid radiologists and improve diagnostic accuracy and efficiency. Current research heavily utilizes single-stage object detection models, particularly variants of the YOLO architecture, often enhanced with attention mechanisms to improve performance on datasets like GRAZPEDWRI-DX. This work is significant because it addresses the need for faster and more accurate fracture detection, potentially reducing diagnostic errors and improving patient care, especially in resource-constrained settings. Furthermore, research is exploring semi-supervised and omni-supervised learning techniques to mitigate the need for extensive labeled datasets.