An Interpretable Deep Learning Framework for Multi-Class Dental Disease Classification from Intraoral RGB Images

Abstract

Dental anomalies and diseases are among the most prevalent health concerns world-wide, and their early and precise diagnosis is critical to ensuring effective treatment and improved patient outcomes. Traditional diagnostic approaches, particularly conventional radiography, are often time-consuming and may not provide sufficient diagnostic accuracy. To address these limitations, this study proposes a robust deep learning framework for the automated classification of dental conditions from intraoral RGB images. Three publicly available datasets—Oral Diseases, Oral Infection, and Teeth Dataset—covering a broad spectrum of dental anomalies were utilized. Five state-of-the-art convolutional neuralnetwork (CNN) architectures, namely Efficient-NetB3, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, were systematically evaluated using a unified transfer learning pipeline. Techniques such as stratified 5-fold cross-validation, ensemble inference, focal loss, class weighting, and label smoothing were employed to enhance generalization and mitigate class imbalance. EfficientNetB3 emerged as the optimal model, achieving accuracies of 95.4%, 89.9%, and 99.3% on the three datasets, with Kappa values reaching 0.989. Grad-CAM visualizations confirmed clinically meaningful feature localization, strengthening interpretability. The proposed framework demonstrates strong potential for integration intointelligent clinical decision-support systems, offering an optimal balance between diagnostic accuracy, computational efficiency, and transparency to assist dental practitioners in timely and reliable decision-making.