YOLO11-Driven Deep Learning Approach for Enhanced Detection and Visualization of Wrist Fractures in X-Ray Images

Mubashar Tariq and Kiho Choi ()
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Mubashar Tariq: Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Gyeonggi-do, Republic of Korea
Kiho Choi: Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Gyeonggi-do, Republic of Korea

Mathematics, 2025, vol. 13, issue 9, 1-31

Abstract: Wrist fractures, especially those involving the elbow and distal radius, are the most common injuries in children, teenagers, and young adults, with the highest occurrence rates during adolescence. However, the demand for medical imaging and the shortage of radiologists make it challenging to ensure accurate diagnosis and treatment. This study explores how AI-driven approaches are used to enhance fracture detection and improve diagnostic accuracy. In this paper, we propose the latest version of YOLO (i.e., YOLO11) with an attention module, designed to refine detection correctness. We integrated attention mechanisms, such as Global Attention Mechanism (GAM), channel attention, and spatial attention with Residual Network (ResNet), to enhance feature extraction. Moreover, we developed the ResNet_GAM model, which combines ResNet with GAM to improve feature learning and model performance. In this paper, we apply a data augmentation process to the publicly available GRAZPEDWRI-DX dataset, which is widely used for detecting radial bone fractures in X-ray images of children. Experimental findings indicate that integrating Squeeze-and-Excitation (SE_BLOCK) into YOLO11 significantly increases model efficiency. Our experimental results attain state-of-the-art performance, measured by the mean average precision (mAP50). Through extensive experiments, we found that our model achieved the highest mAP50 of 0.651. Meanwhile, YOLO11 with GAM and ResNet_GAM attained a maximum precision of 0.799 and a recall of 0.639 across all classes on the given dataset. The potential of these models to improve pediatric wrist imaging is significant, as they offer better detection accuracy while still being computationally efficient. Additionally, to help surgeons identify and diagnose fractures in patient wrist X-ray images, we provide a Fracture Detection Web-based Interface based on the result of the proposed method. This interface reduces the risk of misinterpretation and provides valuable information to assist in making surgical decisions.

Keywords: wrist fracture detection; medical imaging; pediatric X-ray; deep learning diagnosis; object localization; radiographic imaging; YOLO (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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