  Wrist trauma and even fractures occur frequently in daily life, particularly
among children who account for a significant proportion of fracture cases.
Before performing surgery, surgeons often request patients to undergo X-ray
imaging first and prepare for it based on the analysis of the radiologist. With
the development of neural networks, You Only Look Once (YOLO) series models
have been widely used in fracture detection as computer-assisted diagnosis
(CAD). In 2023, Ultralytics presented the latest version of the YOLO models,
which has been employed for detecting fractures across various parts of the
body. Attention mechanism is one of the hottest methods to improve the model
performance. This research work proposes YOLOv8-AM, which incorporates the
attention mechanism into the original YOLOv8 architecture. Specifically, we
respectively employ four attention modules, Convolutional Block Attention
Module (CBAM), Global Attention Mechanism (GAM), Efficient Channel Attention
(ECA), and Shuffle Attention (SA), to design the improved models and train them
on GRAZPEDWRI-DX dataset. Experimental results demonstrate that the mean
Average Precision at IoU 50 (mAP 50) of the YOLOv8-AM model based on ResBlock +
CBAM (ResCBAM) increased from 63.6% to 65.8%, which achieves the
state-of-the-art (SOTA) performance. Conversely, YOLOv8-AM model incorporating
GAM obtains the mAP 50 value of 64.2%, which is not a satisfactory enhancement.
Therefore, we combine ResBlock and GAM, introducing ResGAM to design another
new YOLOv8-AM model, whose mAP 50 value is increased to 65.0%. The
implementation code for this study is available on GitHub at
https://github.com/RuiyangJu/Fracture_Detection_Improved_YOLOv8.
