ArticleCancers2024
Classification of Mobile-Based Oral Cancer Images Using the Vision Transformer and the Swin Transformer.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Applying transformer-based deep learning models in image-driven cancer diagnosis: a comprehensive bibliometric analysis of global research trends.Frontiers in oncology · 2026Pooled it
- Article
- Context-Guided Mixture-of-Experts with Multimodal LLMs for Lesion Detection in Buccal Mucosa Images.International dental journal · 2026Article
- EnamelNet-TRiX: A Lesion-Aware Dual-Transformer With Cross-Attention for Early and Advanced Enamel Caries Diagnosis.International dental journal · 2026Article
- Artificial intelligence for diagnosis and triage in oral cancer: a clinician‑centered narrative review.International journal of clinical oncology · 2026Review
- Attention-aided hybrid transformer network for oral squamous cell carcinoma classification using histopathology images.Frontiers in artificial intelligence · 2026Article
- Global, regional, and national burden of lip and oral cavity cancer attributable to smoking, 1990-2021: A secondary dataset analysis of GBD 2021.Tobacco induced diseases · 2026Article
- Research progress on carcinogenic factors and personalized treatment of oral cancer.Discover oncology · 2025Review
- Article
- Gene Swin transformer: new deep learning method for colorectal cancer prognosis using transcriptomic data.Briefings in bioinformatics · 2025Article
- Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo.Biosensors & bioelectronics · 2025Review
- Enhancing food recognition accuracy using hybrid transformer models and image preprocessing techniques.Scientific reports · 2025Article
- Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications.Diagnostics (Basel, Switzerland) · 2025Review
- MLG: a mixed local and global model for brain tumor classification.Frontiers in neuroscience · 2025Article
- An explainable dual-modal diagnostic model for coronary artery disease: a feature-gated approach using tongue and facial image features.Frontiers in artificial intelligence · 2025Article
- Artificial intelligence and the diagnosis of oral cavity cancer and oral potentially malignant disorders from clinical photographs: a narrative review.Frontiers in oral health · 2025Review
- Identification of benign and malignant breast nodules on ultrasound: comparison of multiple deep learning models and model interpretation.Frontiers in oncology · 2025Article
- Incorporating adipose tissue into a CT-based deep learning nomogram to differentiate granulomas from lung adenocarcinomas.iScience · 2024Article
- Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions.Current oncology (Toronto, Ont.) · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Oral cancer, a pervasive and rapidly growing malignant disease, poses a significant global health concern. Early and accurate diagnosis is pivotal for improving patient outcomes. Automatic diagnosis methods based on artificial intelligence have shown promising results in the oral cancer field, but the accuracy still needs to be improved for realistic diagnostic scenarios. Vision Transformers (ViT) have outperformed learning CNN models recently in many computer vision benchmark tasks. This study explores the effectiveness of the Vision Transformer and the Swin Transformer, two cutting-edge variants of the transformer architecture, for the mobile-based oral cancer image classification application. The pre-trained Swin transformer model achieved 88.7% accuracy in the binary classification task, outperforming the ViT model by 2.3%, while the conventional convolutional network model VGG19 and ResNet50 achieved 85.2% and 84.5% accuracy. Our experiments demonstrate that these transformer-based architectures outperform traditional convolutional neural networks in terms of oral cancer image classification, and underscore the potential of the ViT and the Swin Transformer in advancing the state of the art in oral cancer image analysis.
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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.