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RVM-GSM: Classification of OCT Images of Genitourinary Syndrome of Menopause Based on Integrated Model of Local–Global Information Pattern
Genitourinary syndrome of menopause (GSM) is a group of syndromes, including atrophy of the reproductive tract and urinary tract, and sexual dysfunction, caused by decreased levels of hormones, such as estrogen, in women during the transition to, or late stage of, menopause. GSM symptoms can gradual...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10136040/ https://www.ncbi.nlm.nih.gov/pubmed/37106637 http://dx.doi.org/10.3390/bioengineering10040450 |
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author | Song, Kaiwen Wang, Haoran Guo, Xinyu Sun, Mingyang Shao, Yanbin Xue, Songfeng Zhang, Hongwei Zhang, Tianyu |
author_facet | Song, Kaiwen Wang, Haoran Guo, Xinyu Sun, Mingyang Shao, Yanbin Xue, Songfeng Zhang, Hongwei Zhang, Tianyu |
author_sort | Song, Kaiwen |
collection | PubMed |
description | Genitourinary syndrome of menopause (GSM) is a group of syndromes, including atrophy of the reproductive tract and urinary tract, and sexual dysfunction, caused by decreased levels of hormones, such as estrogen, in women during the transition to, or late stage of, menopause. GSM symptoms can gradually become severe with age and menopausal time, seriously affecting the safety, and physical and mental health, of patients. Optical coherence tomography (OCT) systems can obtain images similar to “optical slices” in a non-destructive manner. This paper presents a neural network, called RVM-GSM, to implement automatic classification tasks for different types of GSM-OCT images. The RVM-GSM module uses a convolutional neural network (CNN) and a vision transformer (ViT) to capture local and global features of the GSM-OCT images, respectively, and, then, fuses the two features in a multi-layer perception module to classify the image. In accordance with the practical needs of clinical work, lightweight post-processing is added to the final surface of the RVM-GSM module to compress the module. Experimental results showed that the accuracy rate of RVM-GSM in the GSM-OCT image classification task was 98.2%. This result is better than those of the CNN and Vit models, demonstrating the promise and potential of the application of RVM-GSM in the physical health and hygiene fields for women. |
format | Online Article Text |
id | pubmed-10136040 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101360402023-04-28 RVM-GSM: Classification of OCT Images of Genitourinary Syndrome of Menopause Based on Integrated Model of Local–Global Information Pattern Song, Kaiwen Wang, Haoran Guo, Xinyu Sun, Mingyang Shao, Yanbin Xue, Songfeng Zhang, Hongwei Zhang, Tianyu Bioengineering (Basel) Article Genitourinary syndrome of menopause (GSM) is a group of syndromes, including atrophy of the reproductive tract and urinary tract, and sexual dysfunction, caused by decreased levels of hormones, such as estrogen, in women during the transition to, or late stage of, menopause. GSM symptoms can gradually become severe with age and menopausal time, seriously affecting the safety, and physical and mental health, of patients. Optical coherence tomography (OCT) systems can obtain images similar to “optical slices” in a non-destructive manner. This paper presents a neural network, called RVM-GSM, to implement automatic classification tasks for different types of GSM-OCT images. The RVM-GSM module uses a convolutional neural network (CNN) and a vision transformer (ViT) to capture local and global features of the GSM-OCT images, respectively, and, then, fuses the two features in a multi-layer perception module to classify the image. In accordance with the practical needs of clinical work, lightweight post-processing is added to the final surface of the RVM-GSM module to compress the module. Experimental results showed that the accuracy rate of RVM-GSM in the GSM-OCT image classification task was 98.2%. This result is better than those of the CNN and Vit models, demonstrating the promise and potential of the application of RVM-GSM in the physical health and hygiene fields for women. MDPI 2023-04-06 /pmc/articles/PMC10136040/ /pubmed/37106637 http://dx.doi.org/10.3390/bioengineering10040450 Text en © 2023 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 Song, Kaiwen Wang, Haoran Guo, Xinyu Sun, Mingyang Shao, Yanbin Xue, Songfeng Zhang, Hongwei Zhang, Tianyu RVM-GSM: Classification of OCT Images of Genitourinary Syndrome of Menopause Based on Integrated Model of Local–Global Information Pattern |
title | RVM-GSM: Classification of OCT Images of Genitourinary Syndrome of Menopause Based on Integrated Model of Local–Global Information Pattern |
title_full | RVM-GSM: Classification of OCT Images of Genitourinary Syndrome of Menopause Based on Integrated Model of Local–Global Information Pattern |
title_fullStr | RVM-GSM: Classification of OCT Images of Genitourinary Syndrome of Menopause Based on Integrated Model of Local–Global Information Pattern |
title_full_unstemmed | RVM-GSM: Classification of OCT Images of Genitourinary Syndrome of Menopause Based on Integrated Model of Local–Global Information Pattern |
title_short | RVM-GSM: Classification of OCT Images of Genitourinary Syndrome of Menopause Based on Integrated Model of Local–Global Information Pattern |
title_sort | rvm-gsm: classification of oct images of genitourinary syndrome of menopause based on integrated model of local–global information pattern |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10136040/ https://www.ncbi.nlm.nih.gov/pubmed/37106637 http://dx.doi.org/10.3390/bioengineering10040450 |
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