Tongue Color Classification Based on Convolutional Neural Network

Niu Guangyu; Wang Caiqun; Yan Bo; Pan Yong

doi:10.1007/978-3-030-73103-8_46

Tongue Color Classification Based on Convolutional Neural Network

Niu Guangyu, Wang Caiqun, Yan Bo, Pan Yong^*

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Tongue color classification plays an important role in traditional Chinese medicine. Tongue color is closely related to the physical condition of patients, so it can help doctors to diagnose patients accurately. However, it is difficult to distinguish between different tongue colors. Therefore, it is necessary to develop an effective method to extract high-dimensional tongue color features. Based on deep learning, this paper proposes a new method to improve the accuracy of tongue color classification. Firstly, the semantic convolutional neural network (CNN) is used to extract the tongue image from the background. Then the CNN model is used to extract the tongue color features, and the center loss function is used to enhance the feature discrimination during the training. Experimental results of different verification indexes show that the accuracy of tongue color classification can be improved by the semantic based CNN and center loss function.

Original language	English
Title of host publication	Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC
Editors	Kohei Arai
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	649-662
Number of pages	14
ISBN (Print)	9783030731021
DOIs	https://doi.org/10.1007/978-3-030-73103-8_46
Publication status	Published - 2021
Event	Future of Information and Communication Conference, FICC 2021 - Virtual, Online Duration: 29 Apr 2021 → 30 Apr 2021

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1364 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Future of Information and Communication Conference, FICC 2021
City	Virtual, Online
Period	29/04/21 → 30/04/21

Keywords

Classification
Convolutional neural network (CNN)
Feature extraction
Semantic segmentation
Tongue color
Traditional chinese medical science

Access to Document

10.1007/978-3-030-73103-8_46

Cite this

Guangyu, N., Caiqun, W., Bo, Y., & Yong, P. (2021). Tongue Color Classification Based on Convolutional Neural Network. In K. Arai (Ed.), Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC (pp. 649-662). (Advances in Intelligent Systems and Computing; Vol. 1364 AISC). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-73103-8_46

Guangyu, Niu ; Caiqun, Wang ; Bo, Yan et al. / Tongue Color Classification Based on Convolutional Neural Network. Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. editor / Kohei Arai. Springer Science and Business Media Deutschland GmbH, 2021. pp. 649-662 (Advances in Intelligent Systems and Computing).

@inproceedings{13d5f29ffd78459294ea36b5e3c941a1,

title = "Tongue Color Classification Based on Convolutional Neural Network",

abstract = "Tongue color classification plays an important role in traditional Chinese medicine. Tongue color is closely related to the physical condition of patients, so it can help doctors to diagnose patients accurately. However, it is difficult to distinguish between different tongue colors. Therefore, it is necessary to develop an effective method to extract high-dimensional tongue color features. Based on deep learning, this paper proposes a new method to improve the accuracy of tongue color classification. Firstly, the semantic convolutional neural network (CNN) is used to extract the tongue image from the background. Then the CNN model is used to extract the tongue color features, and the center loss function is used to enhance the feature discrimination during the training. Experimental results of different verification indexes show that the accuracy of tongue color classification can be improved by the semantic based CNN and center loss function.",

keywords = "Classification, Convolutional neural network (CNN), Feature extraction, Semantic segmentation, Tongue color, Traditional chinese medical science",

author = "Niu Guangyu and Wang Caiqun and Yan Bo and Pan Yong",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.; Future of Information and Communication Conference, FICC 2021 ; Conference date: 29-04-2021 Through 30-04-2021",

year = "2021",

doi = "10.1007/978-3-030-73103-8_46",

language = "English",

isbn = "9783030731021",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "649--662",

editor = "Kohei Arai",

booktitle = "Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC",

address = "Germany",

}

Guangyu, N, Caiqun, W, Bo, Y & Yong, P 2021, Tongue Color Classification Based on Convolutional Neural Network. in K Arai (ed.), Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. Advances in Intelligent Systems and Computing, vol. 1364 AISC, Springer Science and Business Media Deutschland GmbH, pp. 649-662, Future of Information and Communication Conference, FICC 2021, Virtual, Online, 29/04/21. https://doi.org/10.1007/978-3-030-73103-8_46

Tongue Color Classification Based on Convolutional Neural Network. / Guangyu, Niu; Caiqun, Wang; Bo, Yan et al.
Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. ed. / Kohei Arai. Springer Science and Business Media Deutschland GmbH, 2021. p. 649-662 (Advances in Intelligent Systems and Computing; Vol. 1364 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Tongue Color Classification Based on Convolutional Neural Network

AU - Guangyu, Niu

AU - Caiqun, Wang

AU - Bo, Yan

AU - Yong, Pan

PY - 2021

Y1 - 2021

N2 - Tongue color classification plays an important role in traditional Chinese medicine. Tongue color is closely related to the physical condition of patients, so it can help doctors to diagnose patients accurately. However, it is difficult to distinguish between different tongue colors. Therefore, it is necessary to develop an effective method to extract high-dimensional tongue color features. Based on deep learning, this paper proposes a new method to improve the accuracy of tongue color classification. Firstly, the semantic convolutional neural network (CNN) is used to extract the tongue image from the background. Then the CNN model is used to extract the tongue color features, and the center loss function is used to enhance the feature discrimination during the training. Experimental results of different verification indexes show that the accuracy of tongue color classification can be improved by the semantic based CNN and center loss function.

AB - Tongue color classification plays an important role in traditional Chinese medicine. Tongue color is closely related to the physical condition of patients, so it can help doctors to diagnose patients accurately. However, it is difficult to distinguish between different tongue colors. Therefore, it is necessary to develop an effective method to extract high-dimensional tongue color features. Based on deep learning, this paper proposes a new method to improve the accuracy of tongue color classification. Firstly, the semantic convolutional neural network (CNN) is used to extract the tongue image from the background. Then the CNN model is used to extract the tongue color features, and the center loss function is used to enhance the feature discrimination during the training. Experimental results of different verification indexes show that the accuracy of tongue color classification can be improved by the semantic based CNN and center loss function.

KW - Classification

KW - Convolutional neural network (CNN)

KW - Feature extraction

KW - Semantic segmentation

KW - Tongue color

KW - Traditional chinese medical science

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U2 - 10.1007/978-3-030-73103-8_46

DO - 10.1007/978-3-030-73103-8_46

M3 - Conference contribution

AN - SCOPUS:85105961936

SN - 9783030731021

T3 - Advances in Intelligent Systems and Computing

SP - 649

EP - 662

BT - Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC

A2 - Arai, Kohei

PB - Springer Science and Business Media Deutschland GmbH

T2 - Future of Information and Communication Conference, FICC 2021

Y2 - 29 April 2021 through 30 April 2021

ER -

Guangyu N, Caiqun W, Bo Y, Yong P. Tongue Color Classification Based on Convolutional Neural Network. In Arai K, editor, Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. Springer Science and Business Media Deutschland GmbH. 2021. p. 649-662. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-73103-8_46

Tongue Color Classification Based on Convolutional Neural Network

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Keywords

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