ArticleDiagnostics (Basel, Switzerland)2022
Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 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
- Editorial for the Special Issue "Medical Data Processing and Analysis-2nd Edition".Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review.BMC cancer · 2025Article
- Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Viability of Whole-Slide Imaging for Intraoperative Touch Imprint Cytological Diagnosis of Axillary Sentinel Lymph Nodes in Breast Cancer Patients.Diagnostic cytopathology · 2025Article
- A low-cost platform for automated cervical cytology: addressing health and socioeconomic challenges in low-resource settings.Frontiers in medical technology · 2025Article
- Hybrid feature fusion in cervical cancer cytology: a novel dual-module approach framework for lesion detection and classification using radiomics, deep learning, and reproducibility.Frontiers in 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
- Advances in artificial intelligence for the early detection of cervical cancer in adult women: a scoping review.Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia · 2025Article
- Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network Models.Interdisciplinary sciences, computational life sciences · 2024Article
- CerviCell-detector: An object detection approach for identifying the cancerous cells in pap smear images of cervical cancer.Heliyon · 2023Article
- A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images.Diagnostics (Basel, Switzerland) · 2023Article
- Analysis of WSI Images by Hybrid Systems with Fusion Features for Early Diagnosis of Cervical Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- ColpoClassifier: A Hybrid Framework for Classification of the Cervigrams.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Feature Engineering in Colposcopy Image Recognition: A Comparative Study.Bioengineering (Basel, Switzerland) · 2023Article
- Ensemble Learning to Categorize the Bethesda System of Reporting of Cervical Cytology.Journal of cytologyArticle
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
Abstract
The fourth most prevalent cancer in women is cervical cancer, and early detection is crucial for effective treatment and prognostic prediction. Conventional cervical cancer screening and classifying methods are less reliable and accurate as they heavily rely on the expertise of a pathologist. As such, colposcopy is an essential part of preventing cervical cancer. Computer-assisted diagnosis is essential for expanding cervical cancer screening because visual screening results in misdiagnosis and low diagnostic effectiveness due to doctors' increased workloads. Classifying a single cervical cell will overwhelm the physicians, in addition to the existence of overlap between cervical cells, which needs efficient algorithms to separate each cell individually. Focusing on the whole image is the best way and an easy task for the diagnosis. Therefore, looking for new methods to diagnose the whole image is necessary and more accurate. However, existing recognition algorithms do not work well for whole-slide image (WSI) analysis, failing to generalize for different stains and imaging, and displaying subpar clinical-level verification. This paper describes the design of a full ensemble deep learning model for the automatic diagnosis of the WSI. The proposed network discriminates between four classes with high accuracy, reaching up to 99.6%. This work is distinct from existing research in terms of simplicity, accuracy, and speed. It focuses on the whole staining slice image, not on a single cell. The designed deep learning structure considers the slice image with overlapping and non-overlapping cervical cells.
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What OpenQuestion holds
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.