Abstract
Convolutional neural networks using deep learning can automatically identify and locate disease markers, improving the accuracy of diagnosis by doctors. In deep learning models, YOLO, as a representative of single-stage models, has the advantages of high accuracy and high detection speed, and has been widely used in the field of object detection. YOLOv5 uses deep learning technologies such as anchor boxes, focal loss, and data augmentation, combined with improvements such as Dark and SPP algorithms, and has the characteristics of a small model and fast speed, making it suitable for deployment on mobile devices. Based on the YOLOv5 model, this article builds a lightweight model for detecting fracture locations in X-ray imaging. This model can assist doctors in diagnosis, reduce misdiagnosis rates, and has important application value.
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Acknowledgements
This work was supported by the Tianjin Normal University 2022 teaching reform project (No. JG01222079).
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Zhao, X., Wu, Y., Wang, Q., Zhang, M. (2024). Fracture Crack Recognition Based on YOLOv5. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1033. Springer, Singapore. https://doi.org/10.1007/978-981-99-7502-0_61
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DOI: https://doi.org/10.1007/978-981-99-7502-0_61
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