Abstract

Info

Title:: Fracture detection in wrist X-ray image using an improved algorithm based on YOLOv8m

Author(s):: PENG Zhibo; CHEN Yong; CUI Yanrong; School of Computer Science, Yangtze University, Jingzhou 434000, China

Abstract:: Currently, the fracture detection in wrist X-ray image has high misdiagnosis rates and faces the challenge ofinadequate medical resources. To assist doctors in fracture diagnosis, an improved approach based on YOLOv8m for fracturedetection in wrist X-ray image is proposed: (1) a large separable kernel attention mechanism is introduced to extract crucialfeature information while suppressing insignificant ones; (2) residual block is integrated into the attention mechanism toenhance its effectiveness and the model’s generalization ability; (3) switchable atrous convolution is combined with the C2fmodule to expand the model’s receptive field, enabling it to capture multi-scale feature information. Experimental resultsdemonstrate that compared with the improved model based on the advanced YOLOv8l, the proposed approach achieves a1.3% increase in mAP50. Notably, by adopting the more compact YOLOv8m model as the basic model, parameter count isreduced by 14.3%, and the floating-point operations per second is lowered by 42.7%. The proposed model can effectively aidradiologists in detecting fractures in wrist X-ray image.