  Children often suffer wrist injuries in daily life, while fracture injuring
radiologists usually need to analyze and interpret X-ray images before surgical
treatment by surgeons. The development of deep learning has enabled neural
network models to work as computer-assisted diagnosis (CAD) tools to help
doctors and experts in diagnosis. Since the YOLOv8 models have obtained the
satisfactory success in object detection tasks, it has been applied to fracture
detection. The Global Context (GC) block effectively models the global context
in a lightweight way, and incorporating it into YOLOv8 can greatly improve the
model performance. This paper proposes the YOLOv8+GC model for fracture
detection, which is an improved version of the YOLOv8 model with the GC block.
Experimental results demonstrate that compared to the original YOLOv8 model,
the proposed YOLOv8-GC model increases the mean average precision calculated at
intersection over union threshold of 0.5 (mAP 50) from 63.58% to 66.32% on the
GRAZPEDWRI-DX dataset, achieving the state-of-the-art (SOTA) level. The
implementation code for this work is available on GitHub at
https://github.com/RuiyangJu/YOLOv8_Global_Context_Fracture_Detection.
