ArticleBioscience reports2018
Automatic classification of cervical cancer from cytological images by using convolutional neural network.
Article in Bioscience reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review on deep learning-based automated cancer diagnosis models.Journal of cellular and molecular medicine · 2024Pooled it
- Metaheuristic optimization of deep CNNs for multi-class diagnosis of cervical cancer and lymphoma.Scientific reports · 2026Article
- Article
- Multi-scale window transformer for cervical cytopathology image recognition.Computational and structural biotechnology journal · 2024Article
- CytoNet: an efficient dual attention based automatic prediction of cancer sub types in cytology studies.Scientific reports · 2024Article
- Swin-GA-RF: genetic algorithm-based Swin Transformer and random forest for enhancing cervical cancer classification.Frontiers in oncology · 2024Article
- Convolutional Neural Networks for Classifying Cervical Cancer Types Using Histological Images.Journal of digital imaging · 2023Article
- HDFCN: A Robust Hybrid Deep Network Based on Feature Concatenation for Cervical Cancer Diagnosis on WSI Pap Smear Slides.BioMed research international · 2023Article
- Deep convolutional neural networks using an active learning strategy for cervical cancer screening and diagnosis.Frontiers in bioinformatics · 2023Article
- Deep Learning Approaches in Histopathology.Cancers · 2022Review
- Cervical Cell/Clumps Detection in Cytology Images Using Transfer Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Cervix Type and Cervical Cancer Classification System Using Deep Learning Techniques.Medical devices (Auckland, N.Z.) · 2022Article
- A deep hybrid learning pipeline for accurate diagnosis of ovarian cancer based on nuclear morphology.PloS one · 2022Article
- A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT.Multimedia tools and applications · 2022Article
- Mobile-based oral cancer classification for point-of-care screening.Journal of biomedical optics · 2021Article
- Using Dynamic Features for Automatic Cervical Precancer Detection.Diagnostics (Basel, Switzerland) · 2021Article
- Artificial classification of cervical squamous lesions in ThinPrep cytologic tests using a deep convolutional neural network.Oncology letters · 2020Article
- Emerging role of deep learning-based artificial intelligence in tumor pathology.Cancer communications (London, England) · 2020Review
- Opportunities for improving cancer treatment using systems biology.Current opinion in systems biology · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cervical cancer (CC) is one of the most common gynecologic malignancies in the world. The incidence and mortality keep high in some remote and poor medical condition regions in China. In order to improve the current situation and promote the pathologists' diagnostic accuracy of CC in such regions, we tried to propose an intelligent and efficient classification model for CC based on convolutional neural network (CNN) with relatively simple architecture compared with others. The model was trained and tested by two groups of image datasets, respectively, which were original image group with a volume of 3012 datasets and augmented image group with a volume of 108432 datasets. Each group has a number of fixed-size RGB images (227*227) of keratinizing squamous, non-keratinizing squamous, and basaloid squamous. The method of three-folder cross-validation was applied to the model. And the classification accuracy of the models, overall, 93.33% for original image group and 89.48% for augmented image group. The improvement of 3.85% has been achieved by using augmented images as input data for the model. The results got from paired-samples
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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.