Tongue Colour Diagnosis System Using Convolutional Neural Network

Abstract Tongue diagnosis is known as one of the effective and yet noninvasive techniques to evaluate patient’s health condition in traditional oriental medicine such as traditional Chinese medicine and traditional Korean medicine. However, due to ambiguity, practitioners may have different interpre...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2022-08, Vol.2319 (1), p.12033
Main Authors: Tan, Yi Chen, Shahrizal Rusli, M., Kamarudin, Nur Diyana, Ab Rahman, Ab Al-Hadi, Izzat Ullah Sheikh, Usman Ullah Sheikh, Peng Tan, Michael Loong, Shapiai, Mohd Ibrahim
Format: Article
Language:English
Subjects:
Artificial neural networks
Color
Diagnosis
Digital imaging
Evaluation
Feature extraction
Feature recognition
Neural networks
Physics
Support vector machines
Tongue
Traditional Chinese medicine
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract Tongue diagnosis is known as one of the effective and yet noninvasive techniques to evaluate patient’s health condition in traditional oriental medicine such as traditional Chinese medicine and traditional Korean 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 paper, a tongue diagnosis system based on Convolution Neural Network (CNN) 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 colours (i.e. red or pink). Several pre-processing and data augmentation methods have been carried out and evaluated, which include salt-and-pepper noises, rotations and flips. The proposed system achieves accuracy of up to 88.98% from 5-fold cross validation. Compared to the reported baseline Support Vector Machine (SVM) method, the proposed method using CNN results in roughly 30% improvement in recognition accuracy.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2319/1/012033