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Evidence-Based Complementary and Alternative Medicine
Volume 2017 (2017), Article ID 7452427, 12 pages
Research Article

Tongue Images Classification Based on Constrained High Dispersal Network

1MOE Research Center for Software/Hardware Co-Design Engineering, East China Normal University, Shanghai 200062, China
2Department of Computer Science, University of Missouri, Columbia, MO 65211, USA
3Department of TCM Information and Technology Center, Shanghai University of TCM, Shanghai, China
4Department of Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai 201203, China

Correspondence should be addressed to Guitao Cao and Minghua Zhu

Received 31 August 2016; Revised 5 January 2017; Accepted 17 January 2017; Published 30 March 2017

Academic Editor: Jeng-Ren Duann

Copyright © 2017 Dan Meng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.