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Chronic Cervicitis and Cervical Cancer Detection Based on Deep Learning of Colposcopy Images Toward Translational Pharmacology

With the rapid development of deep learning, automatic image recognition is widely used in medical development. In this study, a deep learning convolutional neural network model was developed to recognize and classify chronic cervicitis and cervical cancer. A total of 10,012 colposcopy images of 1,0...

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Detalles Bibliográficos
Autores principales: Huang, Wei, Sun, Shasha, Yu, Zhengyu, Lu, Shanshan, Feng, Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9196041/
https://www.ncbi.nlm.nih.gov/pubmed/35712722
http://dx.doi.org/10.3389/fphar.2022.911962
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author Huang, Wei
Sun, Shasha
Yu, Zhengyu
Lu, Shanshan
Feng, Hao
author_facet Huang, Wei
Sun, Shasha
Yu, Zhengyu
Lu, Shanshan
Feng, Hao
author_sort Huang, Wei
collection PubMed
description With the rapid development of deep learning, automatic image recognition is widely used in medical development. In this study, a deep learning convolutional neural network model was developed to recognize and classify chronic cervicitis and cervical cancer. A total of 10,012 colposcopy images of 1,081 patients from Hunan Provincial People’s Hospital in China were recorded. Five different colposcopy image features of the cervix including chronic cervicitis, intraepithelial lesions, cancer, polypus, and free hyperplastic squamous epithelial tissue were extracted to be applied in our deep learning network convolutional neural network model. However, the result showed a low accuracy (42.16%) due to computer misrecognition of chronic cervicitis, intraepithelial lesions, and free hyperplastic squamous epithelial tissue with high similarity. To optimize this model, we selected two significant feature images: chronic cervicitis and cervical cancer to input into a deep learning network. The result indicates high accuracy and robustness with an accuracy of 95.19%, which can be applied to detect whether the patient has chronic cervicitis or cervical cancer based on the patient’s colposcopy images.
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spelling pubmed-91960412022-06-15 Chronic Cervicitis and Cervical Cancer Detection Based on Deep Learning of Colposcopy Images Toward Translational Pharmacology Huang, Wei Sun, Shasha Yu, Zhengyu Lu, Shanshan Feng, Hao Front Pharmacol Pharmacology With the rapid development of deep learning, automatic image recognition is widely used in medical development. In this study, a deep learning convolutional neural network model was developed to recognize and classify chronic cervicitis and cervical cancer. A total of 10,012 colposcopy images of 1,081 patients from Hunan Provincial People’s Hospital in China were recorded. Five different colposcopy image features of the cervix including chronic cervicitis, intraepithelial lesions, cancer, polypus, and free hyperplastic squamous epithelial tissue were extracted to be applied in our deep learning network convolutional neural network model. However, the result showed a low accuracy (42.16%) due to computer misrecognition of chronic cervicitis, intraepithelial lesions, and free hyperplastic squamous epithelial tissue with high similarity. To optimize this model, we selected two significant feature images: chronic cervicitis and cervical cancer to input into a deep learning network. The result indicates high accuracy and robustness with an accuracy of 95.19%, which can be applied to detect whether the patient has chronic cervicitis or cervical cancer based on the patient’s colposcopy images. Frontiers Media S.A. 2022-05-27 /pmc/articles/PMC9196041/ /pubmed/35712722 http://dx.doi.org/10.3389/fphar.2022.911962 Text en Copyright © 2022 Huang, Sun, Yu, Lu and Feng. 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 Pharmacology
Huang, Wei
Sun, Shasha
Yu, Zhengyu
Lu, Shanshan
Feng, Hao
Chronic Cervicitis and Cervical Cancer Detection Based on Deep Learning of Colposcopy Images Toward Translational Pharmacology
title Chronic Cervicitis and Cervical Cancer Detection Based on Deep Learning of Colposcopy Images Toward Translational Pharmacology
title_full Chronic Cervicitis and Cervical Cancer Detection Based on Deep Learning of Colposcopy Images Toward Translational Pharmacology
title_fullStr Chronic Cervicitis and Cervical Cancer Detection Based on Deep Learning of Colposcopy Images Toward Translational Pharmacology
title_full_unstemmed Chronic Cervicitis and Cervical Cancer Detection Based on Deep Learning of Colposcopy Images Toward Translational Pharmacology
title_short Chronic Cervicitis and Cervical Cancer Detection Based on Deep Learning of Colposcopy Images Toward Translational Pharmacology
title_sort chronic cervicitis and cervical cancer detection based on deep learning of colposcopy images toward translational pharmacology
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9196041/
https://www.ncbi.nlm.nih.gov/pubmed/35712722
http://dx.doi.org/10.3389/fphar.2022.911962
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