📝 SummarXiv

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