Interpretable Attention-Guided Fused Dual-Model Features Framework for Enamel Caries Classification on Clinical Images

Dental enamel caries is among the most prevalent oral diseases worldwide. Early detection is essential, as incipient lesions can be managed with non-invasive therapies. A dataset of 2,000 clinical dental images categorized as Advanced Enamel Caries, Early-Stage Enamel Caries, and No Enamel Caries was curated and expanded to 12,000 images using preprocessing and augmentation. Two transfer learning models, Modified EfficientNetB0 and Modified MobileNetV2, were trained individually, then combined using an attention-guided fusion mechanism. Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to provide visual interpretability.
The Modified EfficientNetB0 and MobileNetV2 models achieved accuracies of 96.33% and 96.25%, respectively. The fused model with Random Forest demonstrated superior performance, achieving 96.92% accuracy, F1-score of 96.92 and ROC AUC of 99.34. Misclassifications were limited to adjacent disease stages, with no severe diagnostic errors.