Multi-label diagnosis of dental conditions from panoramic x-rays using attention-enhanced deep learning.

Abstract

Objective: This study aimed to develop and evaluate automated deep learning models for multi-class classification of dental conditions in panoramic X-ray images, comparing the effectiveness of custom CNN architectures with attention mechanisms, pre-trained models, and hybrid approaches.

Methodology: A dataset of 1,512 panoramic dental X-rays was preprocessed through segmentation, creating 4,764 class-balanced images across four categories: Fillings, Cavity, Implant, and Impacted Tooth. Data augmentation and preprocessing techniques including brightness adjustment, CLAHE enhancement, and normalization were applied. Multiple architectures were evaluated: custom CNN with attention mechanism, pre-trained models (VGG16, ResNet50, Xception) with attention integration, and hybrid CNN-machine learning approaches (CNN + SVM, CNN + Random Forest, CNN + Decision Tree). Performance was assessed using 5-fold cross-validation with accuracy, precision, recall, F1-score, and ROC-AUC metrics.

Results: The hybrid CNN + Random Forest model with preprocessing achieved the highest performance: 90.6% accuracy, 0.987 ROC-AUC, and 0.906 F1-score. Preprocessing consistently improved performance across all architectures, with accuracy gains ranging from 6.3% (VGG16) to 19.4% (ResNet50). The custom CNN with attention mechanism reached 86.0% accuracy, outperforming conventional CNN approaches (76.0%). Among pre-trained models, Xception with preprocessing achieved 79.8% accuracy.

Conclusion: Hybrid CNN-machine learning approaches demonstrated superior performance for dental condition classification compared to end-to-end deep learning models. However, clinical implementation requires addressing the dataset limitation of lacking normal/healthy cases and conducting prospective validation studies across diverse clinical populations to establish real-world effectiveness and safety.