ArticleBioengineering (Basel, Switzerland)2023
High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Application of artificial intelligence in cervical cytology: a systematic review of deep learning models, datasets, and reported metrics.Frontiers in big data · 2025Pooled it
- [Self-supervised classification method for breast pathology images fusing geometric priors and multi-scale attention].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Explainable artificial intelligence with pyramid vision transformer model for multi-class malignant cell classification on cytology slides.Scientific reports · 2026Article
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies.Bioengineering (Basel, Switzerland) · 2025Review
- Leveraging swin transformer with ensemble of deep learning model for cervical cancer screening using colposcopy images.Scientific reports · 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
- Cells Grouping Detection and Confusing Labels Correction on Cervical Pathology Images.Bioengineering (Basel, Switzerland) · 2024Article
- Detection of Cervical Lesion Cell/Clumps Based on Adaptive Feature Extraction.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional cervical cancer diagnosis mainly relies on human papillomavirus (HPV) concentration testing. Considering that HPV concentrations vary from individual to individual and fluctuate over time, this method requires multiple tests, leading to high costs. Recently, some scholars have focused on the method of cervical cytology for diagnosis. However, cervical cancer cells have complex textural characteristics and small differences between different cell subtypes, which brings great challenges for high-precision screening of cervical cancer. In this paper, we propose a high-precision cervical cancer precancerous lesion screening classification method based on ConvNeXt, utilizing self-supervised data augmentation and ensemble learning strategies to achieve cervical cancer cell feature extraction and inter-class discrimination, respectively. We used the Deep Cervical Cytological Levels (DCCL) dataset, which includes 1167 cervical cytology specimens from participants aged 32 to 67, for algorithm training and validation. We tested our method on the DCCL dataset, and the final classification accuracy was 8.85% higher than that of previous advanced models, which means that our method has significant advantages compared to other advanced methods.
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