research-article

Tongue Image Texture Classification Based on Xception

Authors:

Farooq AhmadAuthors Info & Claims

ICCPR '20: Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

Pages 261 - 266

https://doi.org/10.1145/3436369.3436468

Published: 11 January 2021 Publication History

Abstract

Tongue image texture analysis is the main content of inspection diagnostics in traditional Chinese medicine (TCM). The tough and tender classification for tongue image is mainly represented by image texture. The traditional extraction method of texture feature is not robust and is not suitable for variable illumination and different image acquisition equipment. So we select 'Xception' as the main network model to complete feature extraction firstly. Then, two full connection layers and a softmax layer are combined, two dropout layers are added in their middle to form a classification network. The experimental results show that the better accuracy rate can be obtained by our method.

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Cited By

Cao SRuan QWu QLin W(2023)A Non-Invasive Interpretable NAFLD Diagnostic Method Combining TCM Tongue Features2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM58861.2023.10385954(4527-4534)Online publication date: 5-Dec-2023
https://doi.org/10.1109/BIBM58861.2023.10385954
Wang SZheng RWang LZhang M(2023)A MobileNet Based Model for Tongue Shape ClassificationCognitive Systems and Information Processing10.1007/978-981-99-0617-8_44(605-616)Online publication date: 24-Feb-2023
https://doi.org/10.1007/978-981-99-0617-8_44
Yan JChen BGuo RZeng MYan HXu ZWang Y(2022)Tongue Image Texture Classification Based on Image Inpainting and Convolutional Neural NetworkComputational and Mathematical Methods in Medicine10.1155/2022/60666402022(1-11)Online publication date: 15-Dec-2022
https://doi.org/10.1155/2022/6066640

Index Terms

Tongue Image Texture Classification Based on Xception
1. Networks
  1. Network performance evaluation
    1. Network performance modeling

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cover image ACM Other conferences

ICCPR '20: Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

October 2020

552 pages

ISBN:9781450387835

DOI:10.1145/3436369

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Beijing University of Technology

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 January 2021

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ICCPR 2020

ICCPR 2020: 2020 9th International Conference on Computing and Pattern Recognition

October 30 - November 1, 2020

Xiamen, China

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Cited By

Cao SRuan QWu QLin W(2023)A Non-Invasive Interpretable NAFLD Diagnostic Method Combining TCM Tongue Features2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM58861.2023.10385954(4527-4534)Online publication date: 5-Dec-2023
https://doi.org/10.1109/BIBM58861.2023.10385954
Wang SZheng RWang LZhang M(2023)A MobileNet Based Model for Tongue Shape ClassificationCognitive Systems and Information Processing10.1007/978-981-99-0617-8_44(605-616)Online publication date: 24-Feb-2023
https://doi.org/10.1007/978-981-99-0617-8_44
Yan JChen BGuo RZeng MYan HXu ZWang Y(2022)Tongue Image Texture Classification Based on Image Inpainting and Convolutional Neural NetworkComputational and Mathematical Methods in Medicine10.1155/2022/60666402022(1-11)Online publication date: 15-Dec-2022
https://doi.org/10.1155/2022/6066640

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