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

Authors
Park, Ye RangKim, Young JaeJu, WoongNam, KyehyunKim, SoonyungKim, Kwang Gi
Issue Date
Aug-2021
Publisher
Nature Research
Citation
Scientific Reports, v.11, no.1
Journal Title
Scientific Reports
Volume
11
Number
1
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81899
DOI
10.1038/s41598-021-95748-3
ISSN
2045-2322
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. © 2021, The Author(s).
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