Quantum-Inspired Explainable Deep Learning Framework for Early Enamel Caries Classification in Intraoral Photographs

Early detection of enamel caries is crucial for preventive dentistry but remains challenging due to the subtle and subjective nature of visual examination. This study aims to develop and validate a quantum-inspired, explainable deep learning framework for the automated and interpretable classification of enamel caries from intraoral photographs. This study proposed a hybrid framework utilizing two deep learning models: a custom lightweight CNN named DentXCaries and a fine-tuned ResNet50 with squeeze-and-excitation attention. A novel quantum entanglement feature fusion technique was introduced to combine the deep features from both models. The fused features were classified using twelve machine learning classifiers. The model was developed on a public dataset of 2,000 intraoral images categorized into Early-Stage Enamel Caries, Advanced Enamel Caries, and No Enamel Caries. Explainable AI (Grad-CAM) provided visual explanations for predictions. Performance was evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and statistical tests. The QNN-Caries framework demonstrates state-of-the-art accuracy for enamel caries classification while providing crucial visual interpretability. It represents a significant step towards a reliable, transparent, and clinically viable AI-assisted diagnostic tool for routine dental screenings.