ArticleCancer cell international2021
Automatic model for cervical cancer screening based on convolutional neural network: a retrospective, multicohort, multicenter study.
Article in Cancer cell international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Article
- Optimizing cervical cancer diagnosis with a hybrid deep neural network and progressive resizing on pap smear WSIs.Scientific reports · 2026Article
- Role of Three-Dimensional Convolution Neural Networks (3D-CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis.International journal of hematology-oncology and stem cell research · 2026Review
- "I don't see anything specifically about Black/African Americans." Testing an Alzheimer-specific generative AI tool tailored for African American/Black communities.ACM transactions on computing for healthcare · 2026Article
- Diagnostic Accuracy of Artificial Intelligence vs. Oncologist Interpretation in Digital Cervicography for Abnormal Cervical Cytology.Journal of clinical medicine · 2025Article
- Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis.EClinicalMedicine · 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
- Classification of cervical cancer using Dense CapsNet with Seg-UNet and denoising autoencoders.Scientific reports · 2024Article
- Analysis of effectiveness in an artificial intelligent film reading system combined with liquid based cytology examination for cervical cancer screening.American journal of translational research · 2024Article
- Mask region-based CNNs for cervical cancer progression diagnosis on pap smear examinations.Heliyon · 2023Article
- Assessing artificial intelligence enabled liquid-based cytology for triaging HPV-positive women: a population-based cross-sectional study.Acta obstetricia et gynecologica Scandinavica · 2023Article
- Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions.Diagnostics (Basel, Switzerland) · 2023Article
- Prevention Strategies and Early Diagnosis of Cervical Cancer: Current State and Prospects.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Deep convolutional neural networks using an active learning strategy for cervical cancer screening and diagnosis.Frontiers in bioinformatics · 2023Article
- Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Development of a prognostic prediction support system for cervical intraepithelial neoplasia using artificial intelligence-based diagnosis.Journal of gynecologic oncology · 2022Article
- Advancement in Deep Learning Methods for Diagnosis and Prognosis of Cervical Cancer.Current genomics · 2022Review
- An artificial intelligence-assisted diagnostic system improves the accuracy of image diagnosis of uterine cervical lesions.Molecular and clinical oncology · 2022Article
- Artificial neural networks (ANNs) for modeling efficient factors in predicting pap smear screening behavior change stage.BioMedicine · 2022Article
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8 authors.
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Abstract
backgroundThe incidence rates of cervical cancer in developing countries have been steeply increasing while the medical resources for prevention, detection, and treatment are still quite limited. Computer-based deep learning methods can achieve high-accuracy fast cancer screening. Such methods can lead to early diagnosis, effective treatment, and hopefully successful prevention of cervical cancer. In this work, we seek to construct a robust deep convolutional neural network (DCNN) model that can assist pathologists in screening cervical cancer.
methodsThinPrep cytologic test (TCT) images diagnosed by pathologists from many collaborating hospitals in different regions were collected. The images were divided into a training dataset (13,775 images), validation dataset (2301 images), and test dataset (408,030 images from 290 scanned copies) for training and effect evaluation of a faster region convolutional neural network (Faster R-CNN) system.
resultsThe sensitivity and specificity of the proposed cervical cancer screening system was 99.4 and 34.8%, respectively, with an area under the curve (AUC) of 0.67. The model could also distinguish between negative and positive cells. The sensitivity values of the atypical squamous cells of undetermined significance (ASCUS), the low-grade squamous intraepithelial lesion (LSIL), and the high-grade squamous intraepithelial lesions (HSIL) were 89.3, 71.5, and 73.9%, respectively. This system could quickly classify the images and generate a test report in about 3 minutes. Hence, the system can reduce the burden on the pathologists and saves them valuable time to analyze more complex cases.
conclusionsIn our study, a CNN-based TCT cervical-cancer screening model was established through a retrospective study of multicenter TCT images. This model shows improved speed and accuracy for cervical cancer screening, and helps overcome the shortage of medical resources required for cervical cancer screening.
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