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Tongue Images Classification Based on Constrained High Dispersal Network
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. Inspir...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390589/ https://www.ncbi.nlm.nih.gov/pubmed/28465706 http://dx.doi.org/10.1155/2017/7452427 |
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author | Meng, Dan Cao, Guitao Duan, Ye Zhu, Minghua Tu, Liping Xu, Dong Xu, Jiatuo |
author_facet | Meng, Dan Cao, Guitao Duan, Ye Zhu, Minghua Tu, Liping Xu, Dong Xu, Jiatuo |
author_sort | Meng, Dan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5390589 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-53905892017-05-02 Tongue Images Classification Based on Constrained High Dispersal Network Meng, Dan Cao, Guitao Duan, Ye Zhu, Minghua Tu, Liping Xu, Dong Xu, Jiatuo Evid Based Complement Alternat Med Research Article 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. Hindawi 2017 2017-03-30 /pmc/articles/PMC5390589/ /pubmed/28465706 http://dx.doi.org/10.1155/2017/7452427 Text en Copyright © 2017 Dan Meng et al. https://creativecommons.org/licenses/by/4.0/ 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. |
spellingShingle | Research Article Meng, Dan Cao, Guitao Duan, Ye Zhu, Minghua Tu, Liping Xu, Dong Xu, Jiatuo Tongue Images Classification Based on Constrained High Dispersal Network |
title | Tongue Images Classification Based on Constrained High Dispersal Network |
title_full | Tongue Images Classification Based on Constrained High Dispersal Network |
title_fullStr | Tongue Images Classification Based on Constrained High Dispersal Network |
title_full_unstemmed | Tongue Images Classification Based on Constrained High Dispersal Network |
title_short | Tongue Images Classification Based on Constrained High Dispersal Network |
title_sort | tongue images classification based on constrained high dispersal network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390589/ https://www.ncbi.nlm.nih.gov/pubmed/28465706 http://dx.doi.org/10.1155/2017/7452427 |
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