ASC-YOLO: Multi-Scale Feature Fusion and Adaptive Decoupled Head for Fracture Detection in Medical Imaging

Fractures occur frequently in daily life, and before a surgeon implements treatment, the plan needs to be based on the radiologist’s imaging diagnosis of the X-ray. Despite progress in deep learning–based fracture detection, existing methods (e.g., two-stage detectors) face challenges such as small target leakage and sensitivity to background interference in complex medical images. To address these issues, this paper proposes the ASC-YOLO model, which employs the Scale-Sensitive Feature Fusion (SSFF) module to enhance multi-scale information extraction through cross-layer feature interaction. In addition, an Adaptive Decoupled Detection Head (AsDDet) is introduced to decouple the classification and regression tasks of the detection head, thereby improving the localization accuracy of small fracture regions and suppressing background noise. Experiments on a large fracture radiograph dataset, GRAZPEDWRI-DX, demonstrate that ASC-YOLO achieves 61% mAP@50, representing an 8% improvement over the baseline YOLO model (mAP@53%). It attains 95% mAP@50 for the fracture category and 97% mAP@50 for the metal category. Furthermore, the model was evaluated on a tumor dataset to verify its generalization capability. The proposed framework provides reliable technical support for accurate fracture screening, which is expected to reduce missed diagnoses and optimize treatment.