A lightweight wrist fracture detection algorithm based on an enhanced YOLOv8 model for X-ray imaging
10.3969/j.issn.1005-202X.2025.06.006
- VernacularTitle:基于改进YOLOv8的轻量级腕部X光骨折检测算法
- Author:
Rongxiang WANG
1
;
Jingwen ZHAO
1
Author Information
1. 上海工程技术大学电子电气工程学院,上海 201620
- Publication Type:Journal Article
- Keywords:
wrist fracture detection;
YOLOv8;
lightweight model;
deep learning;
medical image analysis
- From:
Chinese Journal of Medical Physics
2025;42(6):740-750
- CountryChina
- Language:Chinese
-
Abstract:
A lightweight object detection algorithm based on an enhanced YOLOv8 framework(YOLOv8-DLE)is proposed to improve the accuracy and efficiency of wrist fracture detection in X-ray images.On the basis of original YOLOv8 framework,the model integrates a dilation-wise residual module,a large separable kernel attention module and an EfficientRepBiPAN for improving the model's ability to detect small targets and manage complex backgrounds while reducing computational cost.Experimental results on the GRAZPEDWRI-DX dataset demonstrate that YOLOv8-DLE outperforms the original YOLOv8,achieving a 3.7%increase in mAP@50 and a 1.7%increase in mAP@50:95,with reductions in parameters from 11.1 M to 10.9 M and GFLOPs from 28.5 to 26.0.The model's compactness and efficiency make it well-suited for embedded devices and remote healthcare systems,particularly in resource-limited environments.YOLOv8-DLE can provide real-time auxiliary diagnostic support for doctors and improve the accuracy and efficiency of diagnosis,showing strong potential for real-time clinical deployment and broad applicability in medical image analysis.
