Fracture detection in wrist X-ray image using an improved algorithm based on YOLOv8m
10.3969/j.issn.1005-202X.2025.04.017
- VernacularTitle:基于YOLOv8m的改进腕部X光片骨折检测算法
- Author:
Zhibo PENG
1
;
Yong CHEN
1
;
Yanrong CUI
1
Author Information
1. 长江大学计算机科学学院,湖北 荆州 434000
- Publication Type:Journal Article
- Keywords:
X-ray;
fracture detection;
deep learning;
YOLOv8
- From:
Chinese Journal of Medical Physics
2025;42(4):542-549
- CountryChina
- Language:Chinese
-
Abstract:
Currently,the fracture detection in wrist X-ray image has high misdiagnosis rates and faces the challenge of inadequate medical resources.To assist doctors in fracture diagnosis,an improved approach based on YOLOv8m for fracture detection in wrist X-ray image is proposed:(1)a large separable kernel attention mechanism is introduced to extract crucial feature information while suppressing insignificant ones;(2)residual block is integrated into the attention mechanism to enhance its effectiveness and the model's generalization ability;(3)switchable atrous convolution is combined with the C2f module to expand the model's receptive field,enabling it to capture multi-scale feature information.Experimental results demonstrate that compared with the improved model based on the advanced YOLOv8l,the proposed approach achieves a 1.3%increase in mAP50.Notably,by adopting the more compact YOLOv8m model as the basic model,parameter count is reduced by 14.3%,and the floating-point operations per second is lowered by 42.7%.The proposed model can effectively aid radiologists in detecting fractures in wrist X-ray image.
