ArticleJournal of imaging2021
A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification.
Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Optimizing cervical cancer diagnosis with a hybrid deep neural network and progressive resizing on pap smear WSIs.Scientific reports · 2026Article
- Use of artificial intelligence in the diagnosis of alterations in cervical cytology: A university population-based observational studyBiomedica : revista del Instituto Nacional de Salud · 2025Observational
- Label credibility correction based on cell morphological differences for cervical cells classification.Scientific reports · 2025Article
- Cells Grouping Detection and Confusing Labels Correction on Cervical Pathology Images.Bioengineering (Basel, Switzerland) · 2024Article
- Facial expression recognition (FER) survey: a vision, architectural elements, and future directions.PeerJ. Computer science · 2024Article
- Beyond the Microscope: A Technological Overture for Cervical Cancer Detection.Diagnostics (Basel, Switzerland) · 2023Review
- 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
- Pap Smear Images Classification Using Machine Learning: A Literature Matrix.Diagnostics (Basel, Switzerland) · 2022Review
- Cytopathology 2.0: How Artificial Intelligence Is Redefining the Future of Cytopathology.Journal of cytologyReview
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals' workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.
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Registered trials
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