ArticleScientific reports2022
Convolutional neural network-based classification of cervical intraepithelial neoplasias using colposcopic image segmentation for acetowhite epithelium.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Neuroimage analysis using artificial intelligence approaches: a systematic review.Medical & biological engineering & computing · 2024Pooled it
- MMTC-Net: Multimodal Temporal Cervical Network for HSIL+ Recognition in Precancer Screening.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Cervical Cytology: Opportunities and Limitations in Screening, Triage, and Diagnostic Support.Diagnostics (Basel, Switzerland) · 2026Review
- Cervical Intraepithelial Neoplasia (CIN1-3) Disease Grading Using a Mixture of Experts Approach.Journal of imaging informatics in medicine · 2026Article
- Deep learning-based automatic segmentation and classification for cervical cancer detection using an improved U-Net and ensemble methods.Scientific reports · 2026Article
- Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- MBLEformer: Multi-Scale Bidirectional Lesion Enhancement Transformer for Cervical Cancer Image Segmentation.Current medical imaging · 2025Article
- A Systematic Review of the Application of Artificial Intelligence in Colposcopy: Diagnostic Accuracy for Cervical Intraepithelial Neoplasia and Cervical Cancer.Clinical Medicine Insights. Oncology · 2025Review
- Deep learning approach for dysphagia detection by syllable-based speech analysis with daily conversations.Scientific reports · 2024Article
- Diagnosis Assistance in Colposcopy by Segmenting Acetowhite Epithelium Using U-Net with Images before and after Acetic Acid Solution Application.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Colposcopy is a test performed to detect precancerous lesions of cervical cancer. Since cervical cancer progresses slowly, finding and treating precancerous lesions helps prevent cervical cancer. In particular, it is clinically important to detect high-grade squamous intraepithelial lesions (HSIL) that require surgical treatment among precancerous lesions of cervix. There have been several studies using convolutional neural network (CNN) for classifying colposcopic images. However, no studies have been reported on using the segmentation technique to detect HSIL. In present study, we aimed to examine whether the accuracy of a CNN model in detecting HSIL from colposcopic images can be improved when segmentation information for acetowhite epithelium is added. Without segmentation information, ResNet-18, 50, and 101 achieved classification accuracies of 70.2%, 66.2%, and 69.3%, respectively. The experts classified the same test set with accuracies of 74.6% and 73.0%. After adding segmentation information of acetowhite epithelium to the original images, the classification accuracies of ResNet-18, 50, and 101 improved to 74.8%, 76.3%, and 74.8%, respectively. We demonstrated that the HSIL detection accuracy improved by adding segmentation information to the CNN model, and the improvement in accuracy was consistent across different ResNets.
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