ArticleJournal of cancer research and clinical oncology2025
ColoViT: a synergistic integration of EfficientNet and vision transformers for advanced colon cancer detection.
Article in Journal of cancer research and clinical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- [Colorectal cancer diagnosis method based on dynamic gland-aware and tissue soft-clustering].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Compact deep learning models for colon histopathology focusing performance and generalization challenges.Scientific reports · 2026Article
- ViTCNN: a robust hybrid CNN-Vision Transformer based deep learning framework for multi-disease diagnosis in women's healthcare.Frontiers in oncology · 2026Article
- Review
- Constructing and validating vision transformer-based assisted detection models for atrophic gastritis: A retrospective study.Science progressArticle
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4 authors.
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Abstract
backgroundColon cancer remains a leading cause of cancer-related mortality globally, highlighting the urgent need for advanced diagnostic methods to improve early detection and patient outcomes.
methodsThis study introduces ColoViT, a hybrid diagnostic framework that synergistically integrates EfficientNet and Vision Transformers. EfficientNet contributes scalability and high performance in feature extraction, while Vision Transformers effectively capture the global contextual information within colonoscopic images.
resultsThe integration of these models enables ColoViT to deliver precise and comprehensive image analysis, significantly improving the detection of precancerous lesions and early-stage colon cancers. The proposed model achieved a recall of 92.4%, precision of 98.9%, F1-score of 98.4%, and an AUC of 99% in our preliminary evaluation.
conclusionColoViT demonstrates superior performance over existing models, offering a robust solution for enhancing the early detection of colon cancer through deep learning-based image analysis.
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