Global Context Modeling in YOLOv8 for Pediatric Wrist Fracture Detection

TL;DR

This paper enhances YOLOv8 for pediatric wrist fracture detection by integrating a global context block, significantly improving accuracy and achieving state-of-the-art results on a relevant dataset.

Contribution

The paper introduces YOLOv8+GC, a novel model that incorporates a lightweight global context block into YOLOv8 for improved fracture detection performance.

Findings

YOLOv8-GC outperforms original YOLOv8 with higher mAP 50 scores.

The model achieves state-of-the-art accuracy on GRAZPEDWRI-DX dataset.

Global context modeling enhances object detection in medical images.

Abstract

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.