Knowledge Commons of Institute of Automation,CAS
Artificial intelligence in tongue diagnosis: Using deep convolutional neural network for recognizing unhealthy tongue with tooth-mark | |
Wang, Xu1; Liu, Jingwei1; Wu, Chaoyong1; Liu, Junhong2; Li, Qianqian1; Chen, Yufeng1; Wang, Xinrong1; Chen, Xinli1; Pang, Xiaohan1; Chang, Binglong1; Lin, Jiaying1; Zhao, Shifeng3; Li, Zhihong1; Deng, Qingqiong3; Lu, Yi4; Zhao, Dongbin4; Chen, Jianxin1 | |
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 | |
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. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论