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

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

Authors :
Xu Wang
Jingwei Liu
Chaoyong Wu
Junhong Liu
Qianqian Li
Yufeng Chen
Xinrong Wang
Xinli Chen
Xiaohan Pang
Binglong Chang
Jiaying Lin
Shifeng Zhao
Zhihong Li
Qingqiong Deng
Yi Lu
Dongbin Zhao
Jianxin Chen
Source :
Computational and Structural Biotechnology Journal, Vol 18, Iss , Pp 973-980 (2020)
Publication Year :
2020
Publisher :
Elsevier, 2020.

Abstract

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.

Details

Language :
English
ISSN :
20010370
Volume :
18
Issue :
973-980
Database :
Directory of Open Access Journals
Journal :
Computational and Structural Biotechnology Journal
Publication Type :
Academic Journal
Accession number :
edsdoj.1c9f66bc8e44049bdda09f7ddac7ce
Document Type :
article
Full Text :
https://doi.org/10.1016/j.csbj.2020.04.002