Ju Rui-Yang ; Chien Chun-Tse ; Chiang Jen-Shiun

Abstract

Wrist trauma and even fractures occur frequently in daily life, particularlyamong children who account for a significant proportion of fracture cases.Before performing surgery, surgeons often request patients to undergo X-rayimaging first, and prepare for the surgery based on the analysis of the X-rayimages. With the development of neural networks, You Only Look Once (YOLO)series models have been widely used in fracture detection for Computer-AssistedDiagnosis, where the YOLOv8 model has obtained the satisfactory results.Applying the attention modules to neural networks is one of the effectivemethods to improve the model performance. This paper proposes YOLOv8-ResCBAM,which incorporates Convolutional Block Attention Module integrated withresblock (ResCBAM) into the original YOLOv8 network architecture. Theexperimental results on the GRAZPEDWRI-DX dataset demonstrate that the meanAverage Precision calculated at Intersection over Union threshold of 0.5 (mAP50) of the proposed model increased from 63.6% of the original YOLOv8 model to65.8%, which achieves the state-of-the-art performance. The implementation codeis available athttps://github.com/RuiyangJu/Fracture_Detection_Improved_YOLOv8.