Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis

The rapid development of intelligent manufacturing provides strong support for the intelligent medical service ecosystem. Researchers are committed to building Wise Information Technology of 120 (WIT 120) for residents and medical personnel with the concept of simple smart medical care and through c...

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Autores principales:	Meng-Yi Li, Ding-Ju Zhu, Wen Xu, Yu-Jie Lin, Kai-Leung Yung, Andrew W. H. Ip
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine (General) R5-920 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/d9e4962c40d24c67aca96859f3a5af30
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spelling	oai:doaj.org-article:d9e4962c40d24c67aca96859f3a5af302021-11-29T00:56:21ZApplication of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis2040-230910.1155/2021/5853128https://doaj.org/article/d9e4962c40d24c67aca96859f3a5af302021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5853128https://doaj.org/toc/2040-2309The rapid development of intelligent manufacturing provides strong support for the intelligent medical service ecosystem. Researchers are committed to building Wise Information Technology of 120 (WIT 120) for residents and medical personnel with the concept of simple smart medical care and through core technologies such as Internet of Things, Big Data Analytics, Artificial Intelligence, and microservice framework, to improve patient safety, medical quality, clinical efficiency, and operational benefits. Among them, how to use computers and deep learning technology to assist in the diagnosis of tongue images and realize intelligent tongue diagnosis has become a major trend. Tongue crack is an important feature of tongue states. Not only does change of tongue crack states reflect objectively and accurately changed circumstances of some typical diseases and TCM syndrome but also semantic segmentation of fissured tongue can combine the other features of tongue states to further improve tongue diagnosis systems’ identification accuracy. Although computer tongue diagnosis technology has made great progress, there are few studies on the fissured tongue, and most of them focus on the analysis of tongue coating and body. In this paper, we do systematic and in-depth researches and propose an improved U-Net network for image semantic segmentation of fissured tongue. By introducing the Global Convolution Network module into the encoder part of U-Net, it solves the problem that the encoder part is relatively simple and cannot extract relatively abstract high-level semantic features. Finally, the method is verified by experiments. The improved U-Net network has a better segmentation effect and higher segmentation accuracy for fissured tongue image dataset. It can be used to design a computer-aided tongue diagnosis system.Meng-Yi LiDing-Ju ZhuWen XuYu-Jie LinKai-Leung YungAndrew W. H. IpHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920 Medical technology R855-855.5
spellingShingle	Medicine (General) R5-920 Medical technology R855-855.5 Meng-Yi Li Ding-Ju Zhu Wen Xu Yu-Jie Lin Kai-Leung Yung Andrew W. H. Ip Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis
description	The rapid development of intelligent manufacturing provides strong support for the intelligent medical service ecosystem. Researchers are committed to building Wise Information Technology of 120 (WIT 120) for residents and medical personnel with the concept of simple smart medical care and through core technologies such as Internet of Things, Big Data Analytics, Artificial Intelligence, and microservice framework, to improve patient safety, medical quality, clinical efficiency, and operational benefits. Among them, how to use computers and deep learning technology to assist in the diagnosis of tongue images and realize intelligent tongue diagnosis has become a major trend. Tongue crack is an important feature of tongue states. Not only does change of tongue crack states reflect objectively and accurately changed circumstances of some typical diseases and TCM syndrome but also semantic segmentation of fissured tongue can combine the other features of tongue states to further improve tongue diagnosis systems’ identification accuracy. Although computer tongue diagnosis technology has made great progress, there are few studies on the fissured tongue, and most of them focus on the analysis of tongue coating and body. In this paper, we do systematic and in-depth researches and propose an improved U-Net network for image semantic segmentation of fissured tongue. By introducing the Global Convolution Network module into the encoder part of U-Net, it solves the problem that the encoder part is relatively simple and cannot extract relatively abstract high-level semantic features. Finally, the method is verified by experiments. The improved U-Net network has a better segmentation effect and higher segmentation accuracy for fissured tongue image dataset. It can be used to design a computer-aided tongue diagnosis system.
format	article
author	Meng-Yi Li Ding-Ju Zhu Wen Xu Yu-Jie Lin Kai-Leung Yung Andrew W. H. Ip
author_facet	Meng-Yi Li Ding-Ju Zhu Wen Xu Yu-Jie Lin Kai-Leung Yung Andrew W. H. Ip
author_sort	Meng-Yi Li
title	Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis
title_short	Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis
title_full	Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis
title_fullStr	Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis
title_full_unstemmed	Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis
title_sort	application of u-net with global convolution network module in computer-aided tongue diagnosis
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/d9e4962c40d24c67aca96859f3a5af30
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Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis

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