An Attention-Enhanced Deep Learning Framework for Multi-Label Dental Findings Classification from Panoramic Radiographs

Panoramic radiographs are widely used in dental practice due to their ability to provide a comprehensive view of the teeth, jaws, and surrounding anatomical structures in a single examination. However, automated interpretation remains challenging because multiple conditions may co-exist within a single image, class distributions are highly imbalanced, and several findings exhibit subtle radiographic characteristics. This study presents a deep learning framework for multi-label dental findings classification using panoramic radiographs from the publicly available VZRAD2 dataset. Following a label curation process, eleven clinically relevant classes were retained, including diseases, treatments, and anatomical structures. The proposed EfficientNet-B4-CBAM model integrates an EfficientNet-B4 backbone with a Convolutional Block Attention Module (CBAM) to enhance feature representation through channel and spatial attention. EfficientNet-B4 and ResNet50 were used as baseline models for comparison under a unified training protocol. The training pipeline incorporates data augmentation, weighted sampling to address class imbalance, AdamW optimization, and Binary Cross-Entropy with Logits loss for multi-label learning. On the validation set, the proposed model achieved the highest micro-F1 score of 0.8567, compared to 0.8424 for EfficientNet-B4 and 0.8469 for ResNet50. ROC analysis showed comparable separability across models, with micro-AUC values of 0.946 (EfficientNet-B4-CBAM), 0.947 (EfficientNet-B4), and 0.960 (ResNet50). Class-wise evaluation indicated strong performance for visually distinct findings such as impacted tooth, implant, filling, and root canal treatment, while anatomically diffuse or underrepresented classes remained more challenging. Grad-CAM visualizations suggest that the model focuses on clinically relevant regions, supporting interpretability. Overall, the results indicate that attention-enhanced convolutional models can provide effective and interpretable support for multi-label dental findings classification. However, the observed performance improvements are modest, and further validation on independent datasets, along with clinical evaluation, is required to confirm generalizability and real-world applicability.