ArticleDiagnostics (Basel, Switzerland)2023
Enhancing Cervical Pre-Cancerous Classification Using Advanced Vision Transformer.
Article in Diagnostics (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.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Article
- Cervical Intraepithelial Neoplasia (CIN1-3) Disease Grading Using a Mixture of Experts Approach.Journal of imaging informatics in medicine · 2026Article
- Cervical image visualization and improvement for clinical support in diagnosis and treatment.Scientific reports · 2025Article
- Advancements in fusion-based deep representation learning for enhanced cervical precancerous lesion classification using biomedical image analysis.Scientific reports · 2025Article
- Leveraging swin transformer with ensemble of deep learning model for cervical cancer screening using colposcopy images.Scientific reports · 2025Article
- Lightweight Low-Rank Adaptation Vision Transformer Framework for Cervical Cancer Detection and Cervix Type Classification.Bioengineering (Basel, Switzerland) · 2024Article
- A Multimodal Transformer Model for Recognition of Images from Complex Laparoscopic Surgical Videos.Diagnostics (Basel, Switzerland) · 2024Article
- Human papillomavirus prevalence and genotypes in Gulf Cooperation Council countries: A scoping review 2017-2024.Qatar medical journal · 2024Article
- A Novel Network-Level Fused Self-Attention Deep Neural Network for Cervical Cancer Classification from Cervicography Images.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the most common types of cancer among in women is cervical cancer. Incidence and fatality rates are steadily rising, particularly in developing nations, due to a lack of screening facilities, experienced specialists, and public awareness. Visual inspection is used to screen for cervical cancer after the application of acetic acid (VIA), histopathology test, Papanicolaou (Pap) test, and human papillomavirus (HPV) test. The goal of this research is to employ a vision transformer (ViT) enhanced with shifted patch tokenization (SPT) techniques to create an integrated and robust system for automatic cervix-type identification. A vision transformer enhanced with shifted patch tokenization is used in this work to learn the distinct features between the three different cervical pre-cancerous types. The model was trained and tested on 8215 colposcopy images of the three types, obtained from the publicly available mobile-ODT dataset. The model was tested on 30% of the whole dataset and it showed a good generalization capability of 91% accuracy. The state-of-the art comparison indicated the outperformance of our model. The experimental results show that the suggested system can be employed as a decision support tool in the detection of the cervical pre-cancer transformation zone, particularly in low-resource settings with limited experience and resources.
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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.