A lightweight pediatric wrist fracture detection algorithm based on improved YOLOv12
ZHANG Chenghao
QIU Dawei
LIU Jing
Abstract：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.
Keywords：YOLOv12Fracture detectionPediatric wrist fractureLightweight networkComputer aided diagnosis
Publication Date：2026-04-30
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：8( 111-118 )