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Research and application of tongue and face diagnosis based on deep learning

OBJECTIVE: To explore the technical research and application characteristics of deep learning in tongue-facial diagnosis. METHODS: Through summarizing the merits and demerits of current image processing techniques used in the traditional medical tongue and face diagnosis, the research status of deep...

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Autores principales: Feng, Li, Huang, Zong Hai, Zhong, Yan Mei, Xiao, WenKe, Wen, Chuan Biao, Song, Hai Bei, Guo, Jin Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490485/
https://www.ncbi.nlm.nih.gov/pubmed/36159155
http://dx.doi.org/10.1177/20552076221124436
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author Feng, Li
Huang, Zong Hai
Zhong, Yan Mei
Xiao, WenKe
Wen, Chuan Biao
Song, Hai Bei
Guo, Jin Hong
author_facet Feng, Li
Huang, Zong Hai
Zhong, Yan Mei
Xiao, WenKe
Wen, Chuan Biao
Song, Hai Bei
Guo, Jin Hong
author_sort Feng, Li
collection PubMed
description OBJECTIVE: To explore the technical research and application characteristics of deep learning in tongue-facial diagnosis. METHODS: Through summarizing the merits and demerits of current image processing techniques used in the traditional medical tongue and face diagnosis, the research status of deep learning in tongue image preprocessing, segmentation, and classification was analyzed and reviewed, and the algorithm was compared and verified with the real tongue and face image. Images of the face and tongue used for diagnosis in conventional medicine were systematically reviewed, from acquisition and pre-processing to segmentation, classification, algorithm comparison, result from analysis, and application. RESULTS: Deep learning improved the speed and accuracy of tongue and face diagnostic image data processing. Among them, the average intersection ratio of U-net and Seg-net models exceeded 0.98, and the segmentation speed ranged from 54 to 58 ms. CONCLUSION: There is no unified standard for lingual-facial diagnosis objectification in terms of image acquisition conditions and image processing methods, thus further research is indispensable. It is feasible to use the images acquired by mobile in the field of medical image analysis by reducing the influence of environmental and other factors on the quality of lingual-facial diagnosis images and improving the efficiency of image processing.
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spelling pubmed-94904852022-09-22 Research and application of tongue and face diagnosis based on deep learning Feng, Li Huang, Zong Hai Zhong, Yan Mei Xiao, WenKe Wen, Chuan Biao Song, Hai Bei Guo, Jin Hong Digit Health Original Research OBJECTIVE: To explore the technical research and application characteristics of deep learning in tongue-facial diagnosis. METHODS: Through summarizing the merits and demerits of current image processing techniques used in the traditional medical tongue and face diagnosis, the research status of deep learning in tongue image preprocessing, segmentation, and classification was analyzed and reviewed, and the algorithm was compared and verified with the real tongue and face image. Images of the face and tongue used for diagnosis in conventional medicine were systematically reviewed, from acquisition and pre-processing to segmentation, classification, algorithm comparison, result from analysis, and application. RESULTS: Deep learning improved the speed and accuracy of tongue and face diagnostic image data processing. Among them, the average intersection ratio of U-net and Seg-net models exceeded 0.98, and the segmentation speed ranged from 54 to 58 ms. CONCLUSION: There is no unified standard for lingual-facial diagnosis objectification in terms of image acquisition conditions and image processing methods, thus further research is indispensable. It is feasible to use the images acquired by mobile in the field of medical image analysis by reducing the influence of environmental and other factors on the quality of lingual-facial diagnosis images and improving the efficiency of image processing. SAGE Publications 2022-09-19 /pmc/articles/PMC9490485/ /pubmed/36159155 http://dx.doi.org/10.1177/20552076221124436 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research
Feng, Li
Huang, Zong Hai
Zhong, Yan Mei
Xiao, WenKe
Wen, Chuan Biao
Song, Hai Bei
Guo, Jin Hong
Research and application of tongue and face diagnosis based on deep learning
title Research and application of tongue and face diagnosis based on deep learning
title_full Research and application of tongue and face diagnosis based on deep learning
title_fullStr Research and application of tongue and face diagnosis based on deep learning
title_full_unstemmed Research and application of tongue and face diagnosis based on deep learning
title_short Research and application of tongue and face diagnosis based on deep learning
title_sort research and application of tongue and face diagnosis based on deep learning
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490485/
https://www.ncbi.nlm.nih.gov/pubmed/36159155
http://dx.doi.org/10.1177/20552076221124436
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