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A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes
OBJECTIVE: Lung cancer is one of the most common malignant tumors in humans. Adenocarcinoma of the lung is another of the most common types of lung cancer. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the fifinal diagnosis of many...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10233124/ https://www.ncbi.nlm.nih.gov/pubmed/37274249 http://dx.doi.org/10.3389/fonc.2023.1172234 |
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author | Liu, Mingyang Li, Liyuan Wang, Haoran Guo, Xinyu Liu, Yunpeng Li, Yuguang Song, Kaiwen Shao, Yanbin Wu, Fei Zhang, Junjie Sun, Nao Zhang, Tianyu Luan, Lan |
author_facet | Liu, Mingyang Li, Liyuan Wang, Haoran Guo, Xinyu Liu, Yunpeng Li, Yuguang Song, Kaiwen Shao, Yanbin Wu, Fei Zhang, Junjie Sun, Nao Zhang, Tianyu Luan, Lan |
author_sort | Liu, Mingyang |
collection | PubMed |
description | OBJECTIVE: Lung cancer is one of the most common malignant tumors in humans. Adenocarcinoma of the lung is another of the most common types of lung cancer. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the fifinal diagnosis of many diseases. Thus, pathological diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far exceeds the number of pathologists, especially in the treatment of lung cancer in less-developed countries. METHODS: This paper proposes a multilayer perceptron model for lung cancer histopathology image detection, which enables the automatic detection of the degree of lung adenocarcinoma infifiltration. For the large amount of local information present in lung cancer histopathology images, MLP IN MLP (MIM) uses a dual data stream input method to achieve a modeling approach that combines global and local information to improve the classifification performance of the model. In our experiments, we collected 780 lung cancer histopathological images and prepared a lung histopathology image dataset to verify the effectiveness of MIM. RESULTS: The MIM achieves a diagnostic accuracy of 95.31% and has a precision, sensitivity, specificity and F1-score of 95.31%, 93.09%, 93.10%, 96.43% and 93.10% respectively, outperforming the diagnostic results of the common network model. In addition, a number of series of extension experiments demonstrated the scalability and stability of the MIM. CONCLUSIONS: In summary, MIM has high classifification performance and substantial potential in lung cancer detection tasks. |
format | Online Article Text |
id | pubmed-10233124 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102331242023-06-02 A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes Liu, Mingyang Li, Liyuan Wang, Haoran Guo, Xinyu Liu, Yunpeng Li, Yuguang Song, Kaiwen Shao, Yanbin Wu, Fei Zhang, Junjie Sun, Nao Zhang, Tianyu Luan, Lan Front Oncol Oncology OBJECTIVE: Lung cancer is one of the most common malignant tumors in humans. Adenocarcinoma of the lung is another of the most common types of lung cancer. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the fifinal diagnosis of many diseases. Thus, pathological diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far exceeds the number of pathologists, especially in the treatment of lung cancer in less-developed countries. METHODS: This paper proposes a multilayer perceptron model for lung cancer histopathology image detection, which enables the automatic detection of the degree of lung adenocarcinoma infifiltration. For the large amount of local information present in lung cancer histopathology images, MLP IN MLP (MIM) uses a dual data stream input method to achieve a modeling approach that combines global and local information to improve the classifification performance of the model. In our experiments, we collected 780 lung cancer histopathological images and prepared a lung histopathology image dataset to verify the effectiveness of MIM. RESULTS: The MIM achieves a diagnostic accuracy of 95.31% and has a precision, sensitivity, specificity and F1-score of 95.31%, 93.09%, 93.10%, 96.43% and 93.10% respectively, outperforming the diagnostic results of the common network model. In addition, a number of series of extension experiments demonstrated the scalability and stability of the MIM. CONCLUSIONS: In summary, MIM has high classifification performance and substantial potential in lung cancer detection tasks. Frontiers Media S.A. 2023-05-18 /pmc/articles/PMC10233124/ /pubmed/37274249 http://dx.doi.org/10.3389/fonc.2023.1172234 Text en Copyright © 2023 Liu, Li, Wang, Guo, Liu, Li, Song, Shao, Wu, Zhang, Sun, Zhang and Luan https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Liu, Mingyang Li, Liyuan Wang, Haoran Guo, Xinyu Liu, Yunpeng Li, Yuguang Song, Kaiwen Shao, Yanbin Wu, Fei Zhang, Junjie Sun, Nao Zhang, Tianyu Luan, Lan A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes |
title | A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes |
title_full | A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes |
title_fullStr | A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes |
title_full_unstemmed | A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes |
title_short | A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes |
title_sort | multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10233124/ https://www.ncbi.nlm.nih.gov/pubmed/37274249 http://dx.doi.org/10.3389/fonc.2023.1172234 |
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