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A Vaginitis Classification Method Based on Multi-Spectral Image Feature Fusion

Vaginitis is one of the commonly encountered diseases of female reproductive tract infections. The clinical diagnosis mainly relies on manual observation under a microscope. There has been some investigation on the classification of vaginitis diseases based on computer-aided diagnosis to reduce the...

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Detalles Bibliográficos
Autores principales: Zhao, Kongya, Gao, Peng, Liu, Sunxiangyu, Wang, Ying, Li, Guitao, Wang, Youzheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840418/
https://www.ncbi.nlm.nih.gov/pubmed/35161875
http://dx.doi.org/10.3390/s22031132
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author Zhao, Kongya
Gao, Peng
Liu, Sunxiangyu
Wang, Ying
Li, Guitao
Wang, Youzheng
author_facet Zhao, Kongya
Gao, Peng
Liu, Sunxiangyu
Wang, Ying
Li, Guitao
Wang, Youzheng
author_sort Zhao, Kongya
collection PubMed
description Vaginitis is one of the commonly encountered diseases of female reproductive tract infections. The clinical diagnosis mainly relies on manual observation under a microscope. There has been some investigation on the classification of vaginitis diseases based on computer-aided diagnosis to reduce the workload of clinical laboratory staff. However, the studies only using RGB images limit the development of vaginitis diagnosis. Through multi-spectral technology, we propose a vaginitis classification algorithm based on multi-spectral image feature layer fusion. Compared with the traditional RGB image, our approach improves the classification accuracy by 11.39%, precision by 15.82%, and recall by 27.25%. Meanwhile, we prove that the level of influence of each spectrum on the disease is distinctive, and the subdivided spectral image is more conducive to the image analysis of vaginitis disease.
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spelling pubmed-88404182022-02-13 A Vaginitis Classification Method Based on Multi-Spectral Image Feature Fusion Zhao, Kongya Gao, Peng Liu, Sunxiangyu Wang, Ying Li, Guitao Wang, Youzheng Sensors (Basel) Article Vaginitis is one of the commonly encountered diseases of female reproductive tract infections. The clinical diagnosis mainly relies on manual observation under a microscope. There has been some investigation on the classification of vaginitis diseases based on computer-aided diagnosis to reduce the workload of clinical laboratory staff. However, the studies only using RGB images limit the development of vaginitis diagnosis. Through multi-spectral technology, we propose a vaginitis classification algorithm based on multi-spectral image feature layer fusion. Compared with the traditional RGB image, our approach improves the classification accuracy by 11.39%, precision by 15.82%, and recall by 27.25%. Meanwhile, we prove that the level of influence of each spectrum on the disease is distinctive, and the subdivided spectral image is more conducive to the image analysis of vaginitis disease. MDPI 2022-02-02 /pmc/articles/PMC8840418/ /pubmed/35161875 http://dx.doi.org/10.3390/s22031132 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhao, Kongya
Gao, Peng
Liu, Sunxiangyu
Wang, Ying
Li, Guitao
Wang, Youzheng
A Vaginitis Classification Method Based on Multi-Spectral Image Feature Fusion
title A Vaginitis Classification Method Based on Multi-Spectral Image Feature Fusion
title_full A Vaginitis Classification Method Based on Multi-Spectral Image Feature Fusion
title_fullStr A Vaginitis Classification Method Based on Multi-Spectral Image Feature Fusion
title_full_unstemmed A Vaginitis Classification Method Based on Multi-Spectral Image Feature Fusion
title_short A Vaginitis Classification Method Based on Multi-Spectral Image Feature Fusion
title_sort vaginitis classification method based on multi-spectral image feature fusion
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840418/
https://www.ncbi.nlm.nih.gov/pubmed/35161875
http://dx.doi.org/10.3390/s22031132
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