Multimodal Deep Learning Approach for Multi-Class Oral Disease Detection and Classification

1.Proposed a Novel hybrid model for multi-class oral disease classification.
2. Combined image features (ViT) with medical contextual knowledge (MedBERT) for improved diagnosis.
3. Applied data preprocessing and augmentation to enhance generalization and reduce overfitting.
4. Achieved 99.71% accuracy, outperforming existing deep learning models.
5. Evaluated performance using precision, recall, F1-score, confusion matrix, and comparative analysis.
6. Demonstrated the model’s clinical potential for accurate and efficient oral disease diagnosis.