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

doi:10.1016/j.csbj.2020.04.002

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 深度强化学习

	Artificial intelligence in tongue diagnosis: Using deep convolutional neural network for recognizing unhealthy tongue with tooth-mark
	Wang, Xu 1; Liu, Jingwei 1; Wu, Chaoyong 1; Liu, Junhong 2; Li, Qianqian 1; Chen, Yufeng 1; Wang, Xinrong 1; Chen, Xinli 1; Pang, Xiaohan 1; Chang, Binglong 1; Lin, Jiaying 1; Zhao, Shifeng 3; Li, Zhihong 1; Deng, Qingqiong 3; Lu, Yi4 ; Zhao, Dongbin4 ; Chen, Jianxin 1
发表期刊	COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
ISSN	2001-0370
	2020
卷号	18 页码:973-980
通讯作者	Chen, Jianxin(cjx@bucm.edu.cn)
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.
关键词	Tooth-marked tongue Traditional Chinese Medicine Convolutional neural network Tongue diagnosis Artificial intelligence
DOI	10.1016/j.csbj.2020.04.002
收录类别	SCI
语种	英语
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]
项目资助者	National Key Research and Development Program ; Beijing Excellent Talent Training Project ; Fundamental Research Funds for the Central Universities
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology
WOS类目	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology
WOS记录号	WOS:000607729500022
出版者	ELSEVIER
引用统计	被引频次：43[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42567
专题	多模态人工智能系统全国重点实验室_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.Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China 3.Beijing Normal Univ, Beijing 100875, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, 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.