ArticleInterdisciplinary sciences, computational life sciences2024
Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network Models.
Article in Interdisciplinary sciences, computational life sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 74 citations in OpenAlex.
- Artificial intelligence for cervical cancer screening and diagnosis using Pap smear images: a systematic review.BMC cancer · 2026Pooled it
- Application of artificial intelligence in cervical cytology: a systematic review of deep learning models, datasets, and reported metrics.Frontiers in big data · 2025Pooled it
- Metaheuristic optimization of deep CNNs for multi-class diagnosis of cervical cancer and lymphoma.Scientific reports · 2026Article
- Article
- Light-XAI: a CADx for explainable cervical cancer detection via attention-based lightweight convolutional neural networks and layer-wise feature fusion.BioData mining · 2026Article
- Advanced analysis and detection of cervical cancer using NASNetLarge.Scientific reports · 2026Article
- DeepInsight-Net: a CBAM-enhanced ResNet50 framework with focal loss for robust cervical cancer classification on multi-center datasets.Frontiers in medicine · 2026Article
- Deep learning radiomics models based on contrast-enhanced transrectal ultrasound for predicting distant metastasis in rectal cancer.Frontiers in oncology · 2026Article
- MGWO-CNN: hyperparameter optimization of CNN classifier for cervical cancer detection using Modified Grey Wolf Optimizer.Scientific reports · 2025Article
- DSMR: Dual-Stream Networks with Refinement Module for Unsupervised Multi-modal Image Registration.Interdisciplinary sciences, computational life sciences · 2025Article
- Artificial intelligence-driven screening, early diagnosis, and treatment strategies for cervical cancer: an overview.Infectious agents and cancer · 2025Review
- Cervical cancer prediction using deformable kernel darknet-53 and depth wise separable convolutional neural networks.Scientific reports · 2025Article
- Research trends of global artificial intelligence application in obstetrics and gynecology from 1999 to 2025: a bibliometric analysis based on web of science.Journal of robotic surgery · 2025Review
- A hybrid compound scaling hypergraph neural network for robust cervical cancer subtype classification using whole slide cytology images.Scientific reports · 2025Article
- Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Comparison of deep transfer learning models for classification of cervical cancer from pap smear images.Scientific reports · 2025Article
- A low-cost platform for automated cervical cytology: addressing health and socioeconomic challenges in low-resource settings.Frontiers in medical technology · 2025Article
- Optimizing cervical cancer diagnosis with accurate cell classification using modified HDFF.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2025Article
- Deep dive into deep learning methods for cervical cancer detection and classification.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2025Review
- Comparative analysis of cervical cancer classification of DPAGCHE-enhanced Pap smear images using convolutional neural network models.PloS one · 2025Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As one of the most common female cancers, cervical cancer often develops years after a prolonged and reversible pre-cancerous stage. Traditional classification algorithms used for detection of cervical cancer often require cell segmentation and feature extraction techniques, while convolutional neural network (CNN) models demand a large dataset to mitigate over-fitting and poor generalization problems. To this end, this study aims to develop deep learning models for automated cervical cancer detection that do not rely on segmentation methods or custom features. Due to limited data availability, transfer learning was employed with pre-trained CNN models to directly operate on Pap smear images for a seven-class classification task. Thorough evaluation and comparison of 13 pre-trained deep CNN models were performed using the publicly available Herlev dataset and the Keras package in Google Collaboratory. In terms of accuracy and performance, DenseNet-201 is the best-performing model. The pre-trained CNN models studied in this paper produced good experimental results and required little computing time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.