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Artificial intelligence in tongue diagnosis: Using deep convolutional neural network for recognizing unhealthy tongue with tooth-mark

文献类型：期刊论文

作者Wang, Xu1; Liu, Jingwei1; Wu, Chaoyong1; Liu, Junhong4; Li, Qianqian1; Chen, Yufeng1; Wang, Xinrong1; Chen, Xinli1; Pang, Xiaohan1; Chang, Binglong1
刊名COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
出版日期2020
卷号18页码:973-980
关键词Tooth-marked tongue Traditional Chinese Medicine Convolutional neural network Tongue diagnosis Artificial intelligence
ISSN号2001-0370
DOI10.1016/j.csbj.2020.04.002
通讯作者Chen, Jianxin(cjx@bucm.edu.cn)
英文摘要Tongue diagnosis plays a pivotal role in traditional Chinese medicine (TCM) for thousands of years. As one of the most important tongue characteristics, tooth-marked tongue is related to spleen deficiency and can greatly contribute to the symptoms differentiation and treatment selection. Yet, the tooth-marked tongue recognition for TCM practitioners is subjective and challenging. Most of the previous studies have concentrated on subjectively selected features of the tooth-marked region and gained accuracy under 80%. In the present study, we proposed an artificial intelligence framework using deep convolutional neural network (CNN) for the recognition of tooth-marked tongue. First, we constructed relatively large datasets with 1548 tongue images captured by different equipments. Then, we used ResNet34 CNN architecture to extract features and perform classifications. The overall accuracy of the models was over 90%. Interestingly, the models can be successfully generalized to images captured by other devices with different illuminations. The good effectiveness and generalization of our framework may provide objective and convenient computer-aided tongue diagnostic method on tracking disease progression and evaluating pharmacological effect from a informatics perspective. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
资助项目National Key Research and Development Program[2017YFC1700106] ; Beijing Excellent Talent Training Project[2018000020124G112] ; Fundamental Research Funds for the Central Universities[2018-JYB-XJQ009] ; Fundamental Research Funds for the Central Universities[3020072120004]
WOS研究方向Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology
语种英语
WOS记录号WOS:000607729500022
出版者ELSEVIER
资助机构National Key Research and Development Program ; Beijing Excellent Talent Training Project ; Fundamental Research Funds for the Central Universities
源URL[http://ir.ia.ac.cn/handle/173211/42566]  
专题复杂系统管理与控制国家重点实验室_深度强化学习
通讯作者Chen, Jianxin
作者单位1.Being Univ Chinese Med, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Beijing Normal Univ, Beijing 100875, Peoples R China
4.Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Xu,Liu, Jingwei,Wu, Chaoyong,et al. Artificial intelligence in tongue diagnosis: Using deep convolutional neural network for recognizing unhealthy tongue with tooth-mark[J]. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL,2020,18:973-980.
APA Wang, Xu.,Liu, Jingwei.,Wu, Chaoyong.,Liu, Junhong.,Li, Qianqian.,...&Chen, Jianxin.(2020).Artificial intelligence in tongue diagnosis: Using deep convolutional neural network for recognizing unhealthy tongue with tooth-mark.COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL,18,973-980.
MLA Wang, Xu,et al."Artificial intelligence in tongue diagnosis: Using deep convolutional neural network for recognizing unhealthy tongue with tooth-mark".COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 18(2020):973-980.

入库方式： OAI收割

来源：自动化研究所

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