Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images

Abstract Cervical cancer is the second most common cancer in women worldwide with a mortality rate of 60%. Cervical cancer begins with no overt signs and has a long latent period, making early detection through regular checkups vitally immportant. In this study, we compare the performance of two dif...

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Autores principales:	Ye Rang Park, Young Jae Kim, Woong Ju, Kyehyun Nam, Soonyung Kim, Kwang Gi Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ad9f8069c7324947a784bd0318075e69
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spelling	oai:doaj.org-article:ad9f8069c7324947a784bd0318075e692021-12-02T18:51:00ZComparison of machine and deep learning for the classification of cervical cancer based on cervicography images10.1038/s41598-021-95748-32045-2322https://doaj.org/article/ad9f8069c7324947a784bd0318075e692021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95748-3https://doaj.org/toc/2045-2322Abstract Cervical cancer is the second most common cancer in women worldwide with a mortality rate of 60%. Cervical cancer begins with no overt signs and has a long latent period, making early detection through regular checkups vitally immportant. In this study, we compare the performance of two different models, machine learning and deep learning, for the purpose of identifying signs of cervical cancer using cervicography images. Using the deep learning model ResNet-50 and the machine learning models XGB, SVM, and RF, we classified 4119 Cervicography images as positive or negative for cervical cancer using square images in which the vaginal wall regions were removed. The machine learning models extracted 10 major features from a total of 300 features. All tests were validated by fivefold cross-validation and receiver operating characteristics (ROC) analysis yielded the following AUCs: ResNet-50 0.97(CI 95% 0.949–0.976), XGB 0.82(CI 95% 0.797–0.851), SVM 0.84(CI 95% 0.801–0.854), RF 0.79(CI 95% 0.804–0.856). The ResNet-50 model showed a 0.15 point improvement (p < 0.05) over the average (0.82) of the three machine learning methods. Our data suggest that the ResNet-50 deep learning algorithm could offer greater performance than current machine learning models for the purpose of identifying cervical cancer using cervicography images.Ye Rang ParkYoung Jae KimWoong JuKyehyun NamSoonyung KimKwang Gi KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ye Rang Park Young Jae Kim Woong Ju Kyehyun Nam Soonyung Kim Kwang Gi Kim Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images
description	Abstract Cervical cancer is the second most common cancer in women worldwide with a mortality rate of 60%. Cervical cancer begins with no overt signs and has a long latent period, making early detection through regular checkups vitally immportant. In this study, we compare the performance of two different models, machine learning and deep learning, for the purpose of identifying signs of cervical cancer using cervicography images. Using the deep learning model ResNet-50 and the machine learning models XGB, SVM, and RF, we classified 4119 Cervicography images as positive or negative for cervical cancer using square images in which the vaginal wall regions were removed. The machine learning models extracted 10 major features from a total of 300 features. All tests were validated by fivefold cross-validation and receiver operating characteristics (ROC) analysis yielded the following AUCs: ResNet-50 0.97(CI 95% 0.949–0.976), XGB 0.82(CI 95% 0.797–0.851), SVM 0.84(CI 95% 0.801–0.854), RF 0.79(CI 95% 0.804–0.856). The ResNet-50 model showed a 0.15 point improvement (p < 0.05) over the average (0.82) of the three machine learning methods. Our data suggest that the ResNet-50 deep learning algorithm could offer greater performance than current machine learning models for the purpose of identifying cervical cancer using cervicography images.
format	article
author	Ye Rang Park Young Jae Kim Woong Ju Kyehyun Nam Soonyung Kim Kwang Gi Kim
author_facet	Ye Rang Park Young Jae Kim Woong Ju Kyehyun Nam Soonyung Kim Kwang Gi Kim
author_sort	Ye Rang Park
title	Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images
title_short	Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images
title_full	Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images
title_fullStr	Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images
title_full_unstemmed	Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images
title_sort	comparison of machine and deep learning for the classification of cervical cancer based on cervicography images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ad9f8069c7324947a784bd0318075e69
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Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images

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