CaviScanNet: AI-Powered Cavity Detection, Segmentation, and Diagnosis with BERT Recommendations

This paper introduces a deep learning-based system for dental X-ray analysis aimed at automating cavity detection, severity classification, and providing personalized recommendations. Using Mask R-CNN, the system detects cavities and segments their affected areas, while ResNet-50 classifies the severity of caries into superficial, medium, or deep categories. A fine-tuned BERT-based recommendation system then offers tailored advice based on severity and potential causes such as poor hygiene or diet. The solution reduces manual diagnostic effort, enhances accuracy, and provides actionable insights, which can be deployed via a web interface for remote accessibility and clinical integration, thus advancing dental care and early intervention.The results shows that the detection is highly accurate with 89.2% mAP (Mean Average Precision), the segmentation accuracy by DSC (Dice Similarity Coefficient) was 91.5%, the classification produced 92% validation accuracy among the superficial, medium, deep caries and recommendation had a 90% relevance score matched with dentists advice which is  a one-of-a-kind feature.