基于改进YOLOv12的轻量级儿童腕部骨折的检测算法OA

To address the issues of blurred boundaries and low detection accuracy for subtle fractures in Child's wrist X-ray ima-ges,as well as the high computational cost faced by some computer aided diagnosis(CAD)methods,we proposed a lightweight frac-ture detection algorithm based on improved YOLOv12.Firstly,a synergistic multi-scale backbone(SMSB)was constructed to enhance the collaborative extraction of shallow spatial details and deep semantic information.Secondly,a contextual detail alignment(CDA)module was designed to efficiently fuse multi-scale features.Finally,a lightweight bounding box quality prediction head(LBBQP-Head)was proposed to mitigate the mismatch between classification confidence and localization accuracy.Experimental results demon-strated that the proposed method achieved a mAP50 of 64.98%on the GRAZPEDWRI-DX dataset,outperformed the baseline and main-stream models including YOLOv8 and YOLOv11,while reducing model parameters by 73.41%.Furthermore,experiments on the HBFMID dataset validated the generalization capability of the proposed algorithm.This study can provide a high-precision and efficient technical solution for intelligent computer aided fracture diagnosis,specifically tailored for primary healthcare institutions.