ArticleDiagnostics (Basel, Switzerland)2022
A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images.
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 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 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
- An interactive mindmap of blood film images for automated malaria diagnosis: a comprehensive metadata repository of available datasets.Bioinformatics advances · 2026Review
- Recent advances in applications of machine learning in cervical cancer research: a focus on prediction models.Obstetrics & gynecology science · 2025Article
- A low-cost platform for automated cervical cytology: addressing health and socioeconomic challenges in low-resource settings.Frontiers in medical technology · 2025Article
- 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
- BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation.Scientific data · 2024Article
- Quantitative Structural Analysis of Hyperchromatic Crowded Cell Groups in Cervical Cytology: Overcoming Diagnostic Pitfalls.Cancers · 2024Article
- CerviCell-detector: An object detection approach for identifying the cancerous cells in pap smear images of cervical cancer.Heliyon · 2023Article
- Automated Assessment of Digital Images of Uterine Cervix Captured Using Transvaginal Device-A Pilot Study.Diagnostics (Basel, Switzerland) · 2023Article
- Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis.Frontiers in oncology · 2023Review
- Radiology 2040.Radiology · 2023Article
- Pap Smear Images Classification Using Machine Learning: A Literature Matrix.Diagnostics (Basel, Switzerland) · 2022Review
- Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors.
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Abstract
Routine Pap smears can facilitate early detection of cervical cancer and improve patient outcomes. The objective of this work is to develop an automated, clinically viable deep neural network for the multi-class Bethesda System diagnosis of multi-cell images in Liquid Pap smear samples. 8 deep learning models were trained on a publicly available multi-class SurePath preparation dataset. This included the 5 best-performing transfer learning models, an ensemble, a novel convolutional neural network (CNN), and a CNN + autoencoder (AE). Additionally, each model was tested on a novel ThinPrep Pap dataset to determine model generalizability across different liquid Pap preparation methods with and without Deep CORAL domain adaptation. All models achieved accuracies >90% when classifying SurePath images. The AE CNN model, 99.80% smaller than the average transfer model, maintained an accuracy of 96.54%. During consecutive training attempts, individual transfer models had high variability in performance, whereas the CNN, AE CNN, and ensemble did not. ThinPrep Pap classification accuracies were notably lower but increased with domain adaptation, with ResNet101 achieving the highest accuracy at 92.65%. This indicates a potential area for future improvement: development of a globally relevant model that can function across different slide preparation methods.
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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.