Computerized tongue coating nature diagnosis using convolutional neural network

Shengyu Fu; Hong Zheng; Zijiang Yang; Bo Yan; Hongyi Su; Yiping Liu

doi:10.1109/ICBDA.2017.8078732

Computerized tongue coating nature diagnosis using convolutional neural network

Shengyu Fu, Hong Zheng, Zijiang Yang, Bo Yan, Hongyi Su, Yiping Liu

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Citations (Scopus)

Abstract

Tongue coating nature inspection is an essential part in the tongue diagnosis of Traditional Chinese Medicine (TCM). However, it has been depending on doctors' visual judgment. Although many researches have been done in this field, the issue remains challenging. The approaches are limited to image processing or shallow neural networks. In this paper, we propose to computerize tongue coating nature using deep neural networks. The method combines the characteristics of basic image processing and deep learning. A standard and balanced tongue image dataset is used to validate the proposed method.

Original language	English
Title of host publication	2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	730-734
Number of pages	5
ISBN (Electronic)	9781509036189
DOIs	https://doi.org/10.1109/ICBDA.2017.8078732
Publication status	Published - 20 Oct 2017
Event	2nd IEEE International Conference on Big Data Analysis, ICBDA 2017 - Beijing, China Duration: 10 Mar 2017 → 12 Mar 2017

Publication series

Name	2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017

Conference

Conference	2nd IEEE International Conference on Big Data Analysis, ICBDA 2017
Country/Territory	China
City	Beijing
Period	10/03/17 → 12/03/17

Keywords

Convolutional Neural Network(CNN)
Deep Learning
Image classification
tongue coating nature

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10.1109/ICBDA.2017.8078732

Cite this

Fu, S., Zheng, H., Yang, Z., Yan, B., Su, H., & Liu, Y. (2017). Computerized tongue coating nature diagnosis using convolutional neural network. In 2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017 (pp. 730-734). Article 8078732 (2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBDA.2017.8078732

@inproceedings{20cc0378a13b416facd42f5934a67720,

title = "Computerized tongue coating nature diagnosis using convolutional neural network",

abstract = "Tongue coating nature inspection is an essential part in the tongue diagnosis of Traditional Chinese Medicine (TCM). However, it has been depending on doctors' visual judgment. Although many researches have been done in this field, the issue remains challenging. The approaches are limited to image processing or shallow neural networks. In this paper, we propose to computerize tongue coating nature using deep neural networks. The method combines the characteristics of basic image processing and deep learning. A standard and balanced tongue image dataset is used to validate the proposed method.",

keywords = "Convolutional Neural Network(CNN), Deep Learning, Image classification, tongue coating nature",

author = "Shengyu Fu and Hong Zheng and Zijiang Yang and Bo Yan and Hongyi Su and Yiping Liu",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd IEEE International Conference on Big Data Analysis, ICBDA 2017 ; Conference date: 10-03-2017 Through 12-03-2017",

year = "2017",

month = oct,

day = "20",

doi = "10.1109/ICBDA.2017.8078732",

language = "English",

series = "2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "730--734",

booktitle = "2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017",

address = "United States",

}

Fu, S, Zheng, H, Yang, Z, Yan, B , Su, H & Liu, Y 2017, Computerized tongue coating nature diagnosis using convolutional neural network. in 2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017., 8078732, 2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017, Institute of Electrical and Electronics Engineers Inc., pp. 730-734, 2nd IEEE International Conference on Big Data Analysis, ICBDA 2017, Beijing, China, 10/03/17. https://doi.org/10.1109/ICBDA.2017.8078732

Computerized tongue coating nature diagnosis using convolutional neural network. / Fu, Shengyu; Zheng, Hong; Yang, Zijiang et al.
2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 730-734 8078732 (2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Computerized tongue coating nature diagnosis using convolutional neural network

AU - Fu, Shengyu

AU - Zheng, Hong

AU - Yang, Zijiang

AU - Yan, Bo

AU - Su, Hongyi

AU - Liu, Yiping

PY - 2017/10/20

Y1 - 2017/10/20

N2 - Tongue coating nature inspection is an essential part in the tongue diagnosis of Traditional Chinese Medicine (TCM). However, it has been depending on doctors' visual judgment. Although many researches have been done in this field, the issue remains challenging. The approaches are limited to image processing or shallow neural networks. In this paper, we propose to computerize tongue coating nature using deep neural networks. The method combines the characteristics of basic image processing and deep learning. A standard and balanced tongue image dataset is used to validate the proposed method.

AB - Tongue coating nature inspection is an essential part in the tongue diagnosis of Traditional Chinese Medicine (TCM). However, it has been depending on doctors' visual judgment. Although many researches have been done in this field, the issue remains challenging. The approaches are limited to image processing or shallow neural networks. In this paper, we propose to computerize tongue coating nature using deep neural networks. The method combines the characteristics of basic image processing and deep learning. A standard and balanced tongue image dataset is used to validate the proposed method.

KW - Convolutional Neural Network(CNN)

KW - Deep Learning

KW - Image classification

KW - tongue coating nature

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U2 - 10.1109/ICBDA.2017.8078732

DO - 10.1109/ICBDA.2017.8078732

M3 - Conference contribution

AN - SCOPUS:85039979371

T3 - 2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017

SP - 730

EP - 734

BT - 2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE International Conference on Big Data Analysis, ICBDA 2017

Y2 - 10 March 2017 through 12 March 2017

ER -

Fu S, Zheng H, Yang Z, Yan B , Su H, Liu Y. Computerized tongue coating nature diagnosis using convolutional neural network. In 2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 730-734. 8078732. (2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017). doi: 10.1109/ICBDA.2017.8078732

Computerized tongue coating nature diagnosis using convolutional neural network

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