ArticleScientific reports2021
Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Artificial Intelligence in Cervical Cytology: Opportunities and Limitations in Screening, Triage, and Diagnostic Support.Diagnostics (Basel, Switzerland) · 2026Review
- Advanced analysis and detection of cervical cancer using NASNetLarge.Scientific reports · 2026Article
- Image segmentation of cervical grainy sandy patches lesions associated with female genital schistosomiasis using deep convolutional neural network with U-NET architecture.PLoS neglected tropical diseases · 2026Article
- A comprehensive study on cervical cancer diagnosis using deep learning and artificial intelligence techniques.Frontiers in artificial intelligence · 2026Review
- Predictive modeling for cervical cancer: existing AI approaches and the emerging role of vaginal microbiome.Frontiers in network physiology · 2026Article
- MGWO-CNN: hyperparameter optimization of CNN classifier for cervical cancer detection using Modified Grey Wolf Optimizer.Scientific reports · 2025Article
- Article
- Development of an Artificial Intelligence Algorithm for Colposcopic Recognition of Cervical Features - Introduction of an Annotation ProtocolBalkan medical journal · 2025Article
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Breaking barriers: noninvasive AI model for BRAFInternational journal of computer assisted radiology and surgery · 2025Article
- Advanced Feature Extraction for Cervical Cancer Image Classification: Integrating Neural Feature Extraction and AutoInt Models.Sensors (Basel, Switzerland) · 2025Article
- Comparison of deep transfer learning models for classification of cervical cancer from pap smear images.Scientific reports · 2025Article
- Detection of cervical cell based on multi-scale spatial information.Scientific reports · 2025Article
- Identification of CDK2 as a key apoptotic gene for predicting cervical cancer prognosis using bioinformatics and machine learning.American journal of cancer research · 2025Article
- Transforming cervical cancer pathological diagnosis through artificial intelligence: progress, performance, and barriers to clinical implementation.Frontiers in oncology · 2025Review
- BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation.Scientific data · 2024Article
- Article
- CytoNet: an efficient dual attention based automatic prediction of cancer sub types in cytology studies.Scientific reports · 2024Article
- Automated analysis of digital medical images in cervical cancer screening: A systematic review.medRxiv : the preprint server for health sciences · 2024Article
- Usability of a smartphone-compatible, confocal micro-endoscope for cervical cancer screening in resource-limited settings.BMC women's health · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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