DW-YOLO: improved YOLO for bone fracture detection

Bo Liu

doi:10.1117/12.3037465

27 September 2024 DW-YOLO: improved YOLO for bone fracture detection

Bo Liu

Author Affiliations +

Proceedings Volume 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024); 132751T (2024) https://doi.org/10.1117/12.3037465
Event: 6th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 2024, Wuhan, China

Abstract

The prompt and accurate detection of bone fractures is vital in patient care, especially in facilities with few experienced diagnosticians. Addressing this need and utilizing Depthwise convolution (DWConv), this study presents DW-YOLO, a novel adaptation of the YOLO object detection model, fortified with depthwise convolutional layers to enhance fracture identification in medical imaging. Leveraging a diverse dataset of 4,148 cranial images categorized into seven fracture types, DW-YOLO underwent comprehensive training, validation, and testing. The model's architecture enables rapid image processing while maintaining high accuracy, as evidenced by achieving a mAP of 0.889 in the test dataset. In addition, the model size is only 7.5MB. The study confirms DW-YOLO's proficiency in identifying fractures across diverse bone structures, emphasizing its potential to enhance clinical diagnostics.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bo Liu "DW-YOLO: improved YOLO for bone fracture detection", Proc. SPIE 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 132751T (27 September 2024); https://doi.org/10.1117/12.3037465

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KEYWORDS

Bone

Object detection

Convolution

Education and training

Image processing

Data modeling

Biological imaging

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