A study of YOLOv5 algorithm-based analytical model for the diagnosis of spinal disorders

Haoran Jia; Yanhui Huang; Huihui Ye; Yuntao Jia

doi:10.1117/12.2643614

9 October 2022 A study of YOLOv5 algorithm-based analytical model for the diagnosis of spinal disorders

Haoran Jia, Yanhui Huang, Huihui Ye, Yuntao Jia

Proceedings Volume 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022); 122462A (2022) https://doi.org/10.1117/12.2643614
Event: 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 2022, Qingdao, China

Abstract

At present, spinal disease diagnosis mainly relies on manual inspection, which has much problems such as low recognition efficiency and human resources. This paper proposes a spinal disease recognition based on YOLOv5 algorithm analysis target detection model, can help doctors to identify patients more fast and efficient type of illness. While traditional YOLOv5 algorithm adopted by the box testing cannot effectively spinal lesion recognition, so the selection based on the data of the key position, which forecasts task cleverly into the key tasks. And the Head end of YOLOv5 algorithm, loss function and the proportion of the NMS, further improvement makes the model can accurately identify patients with spine illness situation and its type. The experimental results show that YOLOv5 algorithm based on key points regression in spinal MRI image validation test dataset, it concluded that model can correctly identify the disease location and disease types of the average accuracy of 52%, and the key position on the patients correctly identify whether the average accuracy of 87.8%.

Citation Download Citation

Haoran Jia, Yanhui Huang, Huihui Ye, and Yuntao Jia "A study of YOLOv5 algorithm-based analytical model for the diagnosis of spinal disorders", Proc. SPIE 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 122462A (9 October 2022); https://doi.org/10.1117/12.2643614

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