Enhancing Dental Caries Classification with Adversarial Training on Bitewing Radiographs

Accurate classification of dental caries from bitewing radiographs is crucial for effective diagnosis and treatment planning. Traditional diagnostic methods can be subjective and inconsistent, highlighting the potential benefits of adopting computer-aided diagnostic systems. Recent studies have demonstrated the effectiveness of deep learning-based classification using the ICCMS^TM 7-class system, which is pivotal for precise caries diagnosis. Building on these advances, this study aims to enhance ResNet models by incorporating the projected gradient descent (PGD) into the training process. By introducing mild perturbations to augment the clean dataset, the proposed approach aims to enhance both the robustness of the model and its classification performance. The experimental results show that the PGD-augmented ResNet-50 exhibits the most significant performance improvements, with the validation accuracy increasing from 58.20 to 67.20% and the test accuracy improving from 57.14 to 59.18%. Additionally, sensitivity and specificity metrics showed notable gains, highlighting the clinical relevance of the approach. These results suggest that incorporating adversarial training techniques such as PGD can substantially improve the accuracy, robustness, and reliability of deep learning models for dental caries classification, thereby advancing computer-aided diagnostic tools in clinical practice.