Deep-learning based bipedal segmentation in diabetic foot infrared thermography

Hengyang Sun; Zhi Zeng; Bo Cui

doi:10.1117/12.2640170

1 August 2022 Deep-learning based bipedal segmentation in diabetic foot infrared thermography

Hengyang Sun, Zhi Zeng, Bo Cui

Proceedings Volume 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022); 122570I (2022) https://doi.org/10.1117/12.2640170
Event: 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 2022, Guangzhou, China

Abstract

Diabetic foot is a chronic diabetic complication. Infrared thermography can be used to scan the bottom of the foot to obtain a thermographic image, and identifying the location of both feet in the image is a key step. A deep learning based algorithm is proposed for automatic recognition of the obtained thermograms. The plantar thermograms of different patients are made into a dataset, trained using deep learning algorithms, and the trained model is judged by the obtained loss function profile, and then analyzed for bipedal recognition. Finally, the experiments compare the recognition performance of Mask-RCNN and Hybrid-TaskCascade (HTC) deep learning algorithms, and the results show that the former has higher detection speed and accuracy. The recognition rate of the former is 99.96%, which meets the require- ment of high recognition rate of biped in thermograms.

Citation Download Citation

Hengyang Sun, Zhi Zeng, and Bo Cui "Deep-learning based bipedal segmentation in diabetic foot infrared thermography", Proc. SPIE 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 122570I (1 August 2022); https://doi.org/10.1117/12.2640170

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