{"ID":2864564,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23100","arxiv_id":"2509.23100","title":"Deep Learning for Oral Health: Benchmarking ViT, DeiT, BEiT, ConvNeXt, and Swin Transformer","abstract":"Objective: The aim of this study was to systematically evaluate and compare the performance of five state-of-the-art transformer-based architectures - Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), ConvNeXt, Swin Transformer, and Bidirectional Encoder Representation from Image Transformers (BEiT) - for multi-class dental disease classification. The study specifically focused on addressing real-world challenges such as data imbalance, which is often overlooked in existing literature. Study Design: The Oral Diseases dataset was used to train and validate the selected models. Performance metrics, including validation accuracy, precision, recall, and F1-score, were measured, with special emphasis on how well each architecture managed imbalanced classes. Results: ConvNeXt achieved the highest validation accuracy at 81.06, followed by BEiT at 80.00 and Swin Transformer at 79.73, all demonstrating strong F1-scores. ViT and DeiT achieved accuracies of 79.37 and 78.79, respectively, but both struggled particularly with Caries-related classes. Conclusions: ConvNeXt, Swin Transformer, and BEiT showed reliable diagnostic performance, making them promising candidates for clinical application in dental imaging. These findings provide guidance for model selection in future AI-driven oral disease diagnostic tools and highlight the importance of addressing data imbalance in real-world scenarios","short_abstract":"Objective: The aim of this study was to systematically evaluate and compare the performance of five state-of-the-art transformer-based architectures - Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), ConvNeXt, Swin Transformer, and Bidirectional Encoder Representation from Image Transformers (BEiT) -...","url_abs":"https://arxiv.org/abs/2509.23100","url_pdf":"https://arxiv.org/pdf/2509.23100v1","authors":"[\"Ajo Babu George\",\"Sadhvik Bathini\",\"Niranjana S R\"]","published":"2025-09-27T04:17:04Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Vision Transformer\",\"Transformer\"]","has_code":false}