TCM Tongue Color Classification Method under Noisy Labeling

ZHUO Li; SUN Liangliang; ZHANG Hui; LI Xiaoguang; ZHANG Jing

doi:10.11999/JEIT210935

Volume 44 Issue 1

Jan. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(1): 89-98

ZHUO Li, SUN Liangliang, ZHANG Hui, LI Xiaoguang, ZHANG Jing. TCM Tongue Color Classification Method under Noisy Labeling[J]. Journal of Electronics & Information Technology, 2022, 44(1): 89-98. doi: 10.11999/JEIT210935

Citation:

ZHUO Li, SUN Liangliang, ZHANG Hui, LI Xiaoguang, ZHANG Jing. TCM Tongue Color Classification Method under Noisy Labeling[J]. Journal of Electronics & Information Technology, 2022, 44(1): 89-98. doi: 10.11999/JEIT210935

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TCM Tongue Color Classification Method under Noisy Labeling

doi: 10.11999/JEIT210935

1.
Signal and Information Processing Laboratory, Beijing University of Technology, Beijing 100124, China
2.
Beijing Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing University of Technology, Beijing 100124, China

Funds: The National Natural Science Foundation of China (61871006)

Received Date: 2021-09-03
Accepted Date: 2021-12-24
Rev Recd Date: 2021-12-22

Available Online: 2021-12-27

Publish Date: 2022-01-10

Abstract

Abstract

Tongue color is one of the most concerned diagnostic features of tongue diagnosis in Traditional Chinese Medicine (TCM). Automatic and accurate tongue color classification is an important content of the objectification of tongue diagnosis. Due to the vagueness of the visual boundaries between different types of tongue colors and the subjectivity of the doctors, the annotated tongue image data samples often contain noises, which has a negative effect on the training of the tongue color classification model. Therefore, in this paper, a tongue color classification method in TCM with noisy labels is proposed. Firstly, a two-stage data cleaning method is proposed to identify and clean noisy labeled samples. Secondly, a lightweight Convolutional Neural Network (CNN) based on the channel attention mechanism is designed in this paper to achieve accurate classification of tongue color by enhancing the expressiveness of features. Finally, a knowledge distillation strategy with a noise sample filtering mechanism is proposed. This strategy adds a noise sample filtering mechanism led by the teacher network to eliminate further noise samples. At the same time, the teacher network is used to guide the training of the light convolutional neural network to improve the classification performance.The experimental results on the self-established TCM tongue color classification dataset show that the proposed method in this paper can significantly improve the classification accuracy with lower computational complexity, reaching 93.88%.

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References(33)

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