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.

翻译：儿童在日常生活中常发生腕部损伤，而骨折损伤通常需要放射科医生在手术前分析和解读X射线图像。深度学习的发展使得神经网络模型能够作为计算机辅助诊断（CAD）工具，帮助医生和专家进行诊断。由于YOLOv8模型在目标检测任务中取得了令人满意的成功，它已被应用于骨折检测。全局上下文（GC）模块以轻量级方式有效建模全局上下文，将其融入YOLOv8可显著提升模型性能。本文提出用于骨折检测的YOLOv8+GC模型，这是集成GC模块的YOLOv8改进版本。实验结果表明，在GRAZPEDWRI-DX数据集上，相较于原始YOLOv8模型，所提出的YOLOv8-GC模型将交并比阈值为0.5时的平均精度均值（mAP 50）从63.58%提升至66.32%，达到了最先进（SOTA）水平。本工作的实现代码已发布于GitHub：https://github.com/RuiyangJu/YOLOv8_Global_Context_Fracture_Detection。