Pediatric Wrist Fracture Detection Using Feature Context Excitation Modules in X‐Ray Images

Abstract

ABSTRACT Children often suffer wrist trauma in daily life and typically require radiologists to analyse and interpret X‐ray images before undergoing surgical treatment. The development of deep learning has enabled neural networks to serve as computer‐aided diagnosis (CAD) tools, assisting doctors and experts in medical image diagnostics. Since the you only look once version‐8 (YOLOv8) model has achieved satisfactory success in object detection tasks, it has been applied to various fracture detection tasks. This work introduces four variants of feature contexts excitation‐YOLOv8 (FCE‐YOLOv8) model, each incorporating a different FCE module (i.e., modules of squeeze‐and‐excitation (SE), global context (GC), gather‐excite (GE), and Gaussian context transformer (GCT)) to enhance the model performance. Experimental results on the GRAZPEDWRI‐DX dataset demonstrate that our proposed YOLOv8+GC‐M3 model improves the value from 65.78% to 66.32%, outperforming the state‐of‐the‐art (SOTA) model while reducing inference time. Furthermore, our proposed YOLOv8+SE‐M3 model achieves the highest value of 67.07%, exceeding the SOTA performance. The implementation of this work is publicly available at https://github.com/RuiyangJu/FCE‐YOLOv8 .

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