ArticleScientific reports2025
Comparison of deep transfer learning models for classification of cervical cancer from pap smear images.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for cervical cancer screening and diagnosis using Pap smear images: a systematic review.BMC cancer · 2026Pooled it
- Article
- Artificial Intelligence in Cervical Cytology: Opportunities and Limitations in Screening, Triage, and Diagnostic Support.Diagnostics (Basel, Switzerland) · 2026Review
- Metaheuristic optimization of deep CNNs for multi-class diagnosis of cervical cancer and lymphoma.Scientific reports · 2026Article
- Explainable artificial intelligence with pyramid vision transformer model for multi-class malignant cell classification on cytology slides.Scientific reports · 2026Article
- Nondestructive sheet resistance prediction of silver nanowire transparent electrode with convolutional neural network.Scientific reports · 2026Article
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- Histopathological diagnosis of Ovine Pulmonary Adenocarcinoma (OPA) based on ensemble model.BMC veterinary research · 2026Article
- Multi-scale feature integration with enhanced cytomorph for high-accuracy cervical cytology classification.PloS one · 2026Article
- Diagnostic accuracy of an artificial intelligence-driven cytopathological tool in detecting cervical pre-cancerous lesions from Pap smear images.Frontiers in digital health · 2026Article
- Recent advancements in the application of artificial intelligence-based approaches for screening, diagnosis, prognosis and treatment of cervical cancer.Oncology reviews · 2026Review
- Machine Learning Models for Predicting Gynecological Cancers: Advances, Challenges, and Future Directions.Cancers · 2025Review
- Post-variational classical quantum transfer learning for binary classification.Scientific reports · 2025Article
- Comparative analysis of cervical cancer classification of DPAGCHE-enhanced Pap smear images using convolutional neural network models.PloS one · 2025Article
- AI-augmented pathology: the experience of transfer learning and intra-domain data diversity in breast cancer metastasis detection.Frontiers in oncology · 2025Article
- Attention-enhanced deep learning for cervical cytology: combining convolutional networks with multi-head attention and fuzzy logic.Polish journal of radiology · 2025Article
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3 authors.
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Abstract
Cervical cancer is one of the most commonly diagnosed cancers worldwide, and it is particularly prevalent among women living in developing countries. Traditional classification algorithms often require segmentation and feature extraction techniques to detect cervical cancer. In contrast, convolutional neural networks (CNN) models require large datasets to reduce overfitting and poor generalization. Based on limited datasets, transfer learning was applied directly to pap smear images to perform a classification task. A comprehensive comparison of 16 pre-trained models (VGG16, VGG19, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152, ResNet152V2, DenseNet121, DenseNet169, DenseNet201, MobileNet, XceptionNet, InceptionV3, and InceptionResNetV2) were carried out for cervical cancer classification by relying on the Herlev dataset and Sipakmed dataset. A comparison of the results revealed that ResNet50 achieved 95% accuracy both for 2-class classification and for 7-class classification using the Herlev dataset. Based on the Sipakmed dataset, VGG16 obtained an accuracy of 99.95% for 2-class and 5-class classification, DenseNet121 achieved an accuracy of 97.65% for 3-class classification. Our findings indicate that DTL models are suitable for automating cervical cancer screening, providing more accurate and efficient results than manual screening.
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