Tongue colour diagnosis system using convolutional neural network

Tongue diagnosis is known as one of the effective and yet noninvasive technique to evaluate patient’s health condition in traditional oriental medicine such as traditional Chinese medicine. However, due to ambiguity, practitioners may have different interpretation on the tongue colour, body shape an...

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Main Author:	Tan, Yi Chen
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/93139/1/TanYiChenMSKE2020.pdf
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id	my-utm-ep.93139
record_format	uketd_dc
spelling	my-utm-ep.931392021-11-19T03:23:55Z Tongue colour diagnosis system using convolutional neural network 2020 Tan, Yi Chen TK Electrical engineering. Electronics Nuclear engineering Tongue diagnosis is known as one of the effective and yet noninvasive technique to evaluate patient’s health condition in traditional oriental medicine such as traditional Chinese medicine. However, due to ambiguity, practitioners may have different interpretation on the tongue colour, body shape and texture. Thus, research of automatic tongue diagnosis system is needed for aiding practitioners in recognizing the features for tongue diagnosis. In this project, a tongue diagnosis system based on Convolution Neural Network for classifying tongue colours is proposed. The system extracts all relevant information (i.e., features) from three-dimensional digital tongue image and classifies the image into one of the colour (i.e. red or pink). To increase the accuracy of the proposed system, a number of pre-processing and data augmentation are carried out and evaluated. Augmentation techniques evaluated consists of salt-and-pepper noises, rotations and flips. Synthetic one-sided flip has that proven that it increases the average accuracy from 75.41% to 75.72%. Thus, this technique is proposed for data augmentation in tongue diagnosis applications. The proposed system achieved accuracy up to 88.98% and average of 75.72% from 5-fold cross validation, and 0.05 seconds in processing time. 2020 Thesis http://eprints.utm.my/id/eprint/93139/ http://eprints.utm.my/id/eprint/93139/1/TanYiChenMSKE2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135978 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Tan, Yi Chen Tongue colour diagnosis system using convolutional neural network
description	Tongue diagnosis is known as one of the effective and yet noninvasive technique to evaluate patient’s health condition in traditional oriental medicine such as traditional Chinese medicine. However, due to ambiguity, practitioners may have different interpretation on the tongue colour, body shape and texture. Thus, research of automatic tongue diagnosis system is needed for aiding practitioners in recognizing the features for tongue diagnosis. In this project, a tongue diagnosis system based on Convolution Neural Network for classifying tongue colours is proposed. The system extracts all relevant information (i.e., features) from three-dimensional digital tongue image and classifies the image into one of the colour (i.e. red or pink). To increase the accuracy of the proposed system, a number of pre-processing and data augmentation are carried out and evaluated. Augmentation techniques evaluated consists of salt-and-pepper noises, rotations and flips. Synthetic one-sided flip has that proven that it increases the average accuracy from 75.41% to 75.72%. Thus, this technique is proposed for data augmentation in tongue diagnosis applications. The proposed system achieved accuracy up to 88.98% and average of 75.72% from 5-fold cross validation, and 0.05 seconds in processing time.
format	Thesis
qualification_level	Master's degree
author	Tan, Yi Chen
author_facet	Tan, Yi Chen
author_sort	Tan, Yi Chen
title	Tongue colour diagnosis system using convolutional neural network
title_short	Tongue colour diagnosis system using convolutional neural network
title_full	Tongue colour diagnosis system using convolutional neural network
title_fullStr	Tongue colour diagnosis system using convolutional neural network
title_full_unstemmed	Tongue colour diagnosis system using convolutional neural network
title_sort	tongue colour diagnosis system using convolutional neural network
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2020
url	http://eprints.utm.my/id/eprint/93139/1/TanYiChenMSKE2020.pdf
_version_	1747818637287227392

Tongue colour diagnosis system using convolutional neural network

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