Tongue Images Classification Based on Constrained High Dispersal Network.

Dan Meng,Dong Xu,Liping Tu,Guitao Cao,Jiatuo Xu,Minghua Zhu,Ye Duan

doi:10.1155/2017/7452427

Abstract

Computer aided tongue diagnosis has a great potential to play important roles in traditional Chinese medicine (TCM). However, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by deep convolutional neural network (CNN), we propose a novel feature extraction framework called constrained high dispersal neural networks (CHDNet) to extract unbiased features and reduce human labor for tongue diagnosis in TCM. Previous CNN models have mostly focused on learning convolutional filters and adapting weights between them, but these models have two major issues: redundancy and insufficient capability in handling unbalanced sample distribution. We introduce high dispersal and local response normalization operation to address the issue of redundancy. We also add multiscale feature analysis to avoid the problem of sensitivity to deformation. Our proposed CHDNet learns high-level features and provides more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed method on a set of 267 gastritis patients and a control group of 48 healthy volunteers. Test results show that CHDNet is a promising method in tongue image classification for the TCM study.

Highlights

Tongue image classification is a key component in traditional Chinese medicine (TCM)
Inspired by the works of deep learning models and their variants, this paper proposes a framework referred to as constrained high dispersal neural networks (CHDNet) based on PCA convolutional kernels to aid in tongue diagnosis
It should be noticed that when compared with PCANet [23], our CHDNet only shares some similarity in learning PCA kernels, but the structure of our CHDNet and especially the techniques in the feature pooling layer are new and uniquely designed to address the tongue image classification problem and significantly improve its performance

Summary

Introduction

Tongue image classification is a key component in traditional Chinese medicine (TCM). Many single feature extraction methods have been proposed and applied to tongue images analysis. Such methods can exploit useful information based on a simple descriptor such as color, texture, shape, and orientation. Most existing publications have described and applied deep learning models to extract high-level feature representations for a wide range of vision analysis tasks [16] (such as handwritten digit recognition [17], face recognition [18], and object recognition [19]). Inspired by the works of deep learning models and their variants, this paper proposes a framework referred to as constrained high dispersal neural networks (CHDNet) based on PCA convolutional kernels to aid in tongue diagnosis.

Algorithm Overview

Experiments

Comparison of Classification Accuracy Using Different

Findings

Conclusions