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Classification of cervical neoplasms on colposcopic photography using deep learning

Colposcopy is widely used to detect cervical cancers, but experienced physicians who are needed for an accurate diagnosis are lacking in developing countries. Artificial intelligence (AI) has been recently used in computer-aided diagnosis showing remarkable promise. In this study, we developed and v...

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Autores principales: Cho, Bum-Joo, Choi, Youn Jin, Lee, Myung-Je, Kim, Ju Han, Son, Ga-Hyun, Park, Sung-Ho, Kim, Hong-Bae, Joo, Yeon-Ji, Cho, Hye-Yon, Kyung, Min Sun, Park, Young-Han, Kang, Byung Soo, Hur, Soo Young, Lee, Sanha, Park, Sung Taek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7423899/
https://www.ncbi.nlm.nih.gov/pubmed/32788635
http://dx.doi.org/10.1038/s41598-020-70490-4
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author Cho, Bum-Joo
Choi, Youn Jin
Lee, Myung-Je
Kim, Ju Han
Son, Ga-Hyun
Park, Sung-Ho
Kim, Hong-Bae
Joo, Yeon-Ji
Cho, Hye-Yon
Kyung, Min Sun
Park, Young-Han
Kang, Byung Soo
Hur, Soo Young
Lee, Sanha
Park, Sung Taek
author_facet Cho, Bum-Joo
Choi, Youn Jin
Lee, Myung-Je
Kim, Ju Han
Son, Ga-Hyun
Park, Sung-Ho
Kim, Hong-Bae
Joo, Yeon-Ji
Cho, Hye-Yon
Kyung, Min Sun
Park, Young-Han
Kang, Byung Soo
Hur, Soo Young
Lee, Sanha
Park, Sung Taek
author_sort Cho, Bum-Joo
collection PubMed
description Colposcopy is widely used to detect cervical cancers, but experienced physicians who are needed for an accurate diagnosis are lacking in developing countries. Artificial intelligence (AI) has been recently used in computer-aided diagnosis showing remarkable promise. In this study, we developed and validated deep learning models to automatically classify cervical neoplasms on colposcopic photographs. Pre-trained convolutional neural networks were fine-tuned for two grading systems: the cervical intraepithelial neoplasia (CIN) system and the lower anogenital squamous terminology (LAST) system. The multi-class classification accuracies of the networks for the CIN system in the test dataset were 48.6 ± 1.3% by Inception-Resnet-v2 and 51.7 ± 5.2% by Resnet-152. The accuracies for the LAST system were 71.8 ± 1.8% and 74.7 ± 1.8%, respectively. The area under the curve (AUC) for discriminating high-risk lesions from low-risk lesions by Resnet-152 was 0.781 ± 0.020 for the CIN system and 0.708 ± 0.024 for the LAST system. The lesions requiring biopsy were also detected efficiently (AUC, 0.947 ± 0.030 by Resnet-152), and presented meaningfully on attention maps. These results may indicate the potential of the application of AI for automated reading of colposcopic photographs.
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spelling pubmed-74238992020-08-13 Classification of cervical neoplasms on colposcopic photography using deep learning Cho, Bum-Joo Choi, Youn Jin Lee, Myung-Je Kim, Ju Han Son, Ga-Hyun Park, Sung-Ho Kim, Hong-Bae Joo, Yeon-Ji Cho, Hye-Yon Kyung, Min Sun Park, Young-Han Kang, Byung Soo Hur, Soo Young Lee, Sanha Park, Sung Taek Sci Rep Article Colposcopy is widely used to detect cervical cancers, but experienced physicians who are needed for an accurate diagnosis are lacking in developing countries. Artificial intelligence (AI) has been recently used in computer-aided diagnosis showing remarkable promise. In this study, we developed and validated deep learning models to automatically classify cervical neoplasms on colposcopic photographs. Pre-trained convolutional neural networks were fine-tuned for two grading systems: the cervical intraepithelial neoplasia (CIN) system and the lower anogenital squamous terminology (LAST) system. The multi-class classification accuracies of the networks for the CIN system in the test dataset were 48.6 ± 1.3% by Inception-Resnet-v2 and 51.7 ± 5.2% by Resnet-152. The accuracies for the LAST system were 71.8 ± 1.8% and 74.7 ± 1.8%, respectively. The area under the curve (AUC) for discriminating high-risk lesions from low-risk lesions by Resnet-152 was 0.781 ± 0.020 for the CIN system and 0.708 ± 0.024 for the LAST system. The lesions requiring biopsy were also detected efficiently (AUC, 0.947 ± 0.030 by Resnet-152), and presented meaningfully on attention maps. These results may indicate the potential of the application of AI for automated reading of colposcopic photographs. Nature Publishing Group UK 2020-08-12 /pmc/articles/PMC7423899/ /pubmed/32788635 http://dx.doi.org/10.1038/s41598-020-70490-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Cho, Bum-Joo
Choi, Youn Jin
Lee, Myung-Je
Kim, Ju Han
Son, Ga-Hyun
Park, Sung-Ho
Kim, Hong-Bae
Joo, Yeon-Ji
Cho, Hye-Yon
Kyung, Min Sun
Park, Young-Han
Kang, Byung Soo
Hur, Soo Young
Lee, Sanha
Park, Sung Taek
Classification of cervical neoplasms on colposcopic photography using deep learning
title Classification of cervical neoplasms on colposcopic photography using deep learning
title_full Classification of cervical neoplasms on colposcopic photography using deep learning
title_fullStr Classification of cervical neoplasms on colposcopic photography using deep learning
title_full_unstemmed Classification of cervical neoplasms on colposcopic photography using deep learning
title_short Classification of cervical neoplasms on colposcopic photography using deep learning
title_sort classification of cervical neoplasms on colposcopic photography using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7423899/
https://www.ncbi.nlm.nih.gov/pubmed/32788635
http://dx.doi.org/10.1038/s41598-020-70490-4
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