Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Ye Rang | - |
dc.contributor.author | Kim, Young Jae | - |
dc.contributor.author | Ju, Woong | - |
dc.contributor.author | Nam, Kyehyun | - |
dc.contributor.author | Kim, Soonyung | - |
dc.contributor.author | Kim, Kwang Gi | - |
dc.date.accessioned | 2021-10-05T04:42:09Z | - |
dc.date.available | 2021-10-05T04:42:09Z | - |
dc.date.issued | 2021-08-09 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/19836 | - |
dc.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. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Nature Publishing Group | - |
dc.title | Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images | - |
dc.type | Article | - |
dc.publisher.location | 영국 | - |
dc.identifier.doi | 10.1038/s41598-021-95748-3 | - |
dc.identifier.scopusid | 2-s2.0-85112056338 | - |
dc.identifier.wosid | 000683506200046 | - |
dc.identifier.bibliographicCitation | Scientific Reports, v.11, no.1 | - |
dc.citation.title | Scientific Reports | - |
dc.citation.volume | 11 | - |
dc.citation.number | 1 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(31538) 22, Soonchunhyang-ro, Asan-si, Chungcheongnam-do, Republic of Korea+82-41-530-1114
COPYRIGHT 2021 by SOONCHUNHYANG UNIVERSITY ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.