Tongue Colour Diagnosis System Using Convolutional Neural Network

EasyChair Preprint 6908

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.

Keyphrases: Convolutional Neural Network, Hardware Accelerator, Traditional Chinese Medicine, deep learning, tongue colour diagnosis

@booklet{EasyChair:6908,
  author    = {Yi Chen Tan and M. Shahrizal Rusli and Nur Diyana Kamarudin and Ab Al-Hadi Ab Rahman and Usman Ullah Sheikh and Michael Tan Loong Peng and Mohd Ibrahim Shapiai},
  title     = {Tongue Colour Diagnosis System Using Convolutional Neural Network},
  howpublished = {EasyChair Preprint 6908},
  year      = {EasyChair, 2021}}