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Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet
OBJECTIVE: In order to better adapt to clinical applications, this paper proposes a cross-validation decision-making fusion method of Vision Transformer and DenseNet161. METHODS: The dataset is the most critical acetic acid image for clinical diagnosis, and the SR areas are processed by a specific m...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9124126/ https://www.ncbi.nlm.nih.gov/pubmed/35607393 http://dx.doi.org/10.1155/2022/3241422 |
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author | Li, Ping Wang, Xiaoxia Liu, Peizhong Xu, Tianxiang Sun, Pengming Dong, Binhua Xue, Huifeng |
author_facet | Li, Ping Wang, Xiaoxia Liu, Peizhong Xu, Tianxiang Sun, Pengming Dong, Binhua Xue, Huifeng |
author_sort | Li, Ping |
collection | PubMed |
description | OBJECTIVE: In order to better adapt to clinical applications, this paper proposes a cross-validation decision-making fusion method of Vision Transformer and DenseNet161. METHODS: The dataset is the most critical acetic acid image for clinical diagnosis, and the SR areas are processed by a specific method. Then, the Vision Transformer and DenseNet161 models are trained by the fivefold cross-validation method, and the fivefold prediction results corresponding to the two models are fused by different weights. Finally, the five fused results are averaged to obtain the category with the highest probability. RESULTS: The results show that the fusion method in this paper reaches an accuracy rate of 68% for the four classifications of cervical lesions. CONCLUSIONS: It is more suitable for clinical environments, effectively reducing the missed detection rate and ensuring the life and health of patients. |
format | Online Article Text |
id | pubmed-9124126 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-91241262022-05-22 Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet Li, Ping Wang, Xiaoxia Liu, Peizhong Xu, Tianxiang Sun, Pengming Dong, Binhua Xue, Huifeng J Healthc Eng Research Article OBJECTIVE: In order to better adapt to clinical applications, this paper proposes a cross-validation decision-making fusion method of Vision Transformer and DenseNet161. METHODS: The dataset is the most critical acetic acid image for clinical diagnosis, and the SR areas are processed by a specific method. Then, the Vision Transformer and DenseNet161 models are trained by the fivefold cross-validation method, and the fivefold prediction results corresponding to the two models are fused by different weights. Finally, the five fused results are averaged to obtain the category with the highest probability. RESULTS: The results show that the fusion method in this paper reaches an accuracy rate of 68% for the four classifications of cervical lesions. CONCLUSIONS: It is more suitable for clinical environments, effectively reducing the missed detection rate and ensuring the life and health of patients. Hindawi 2022-05-14 /pmc/articles/PMC9124126/ /pubmed/35607393 http://dx.doi.org/10.1155/2022/3241422 Text en Copyright © 2022 Ping Li 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 Li, Ping Wang, Xiaoxia Liu, Peizhong Xu, Tianxiang Sun, Pengming Dong, Binhua Xue, Huifeng Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet |
title | Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet |
title_full | Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet |
title_fullStr | Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet |
title_full_unstemmed | Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet |
title_short | Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet |
title_sort | cervical lesion classification method based on cross-validation decision fusion method of vision transformer and densenet |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9124126/ https://www.ncbi.nlm.nih.gov/pubmed/35607393 http://dx.doi.org/10.1155/2022/3241422 |
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