Automated Detection and Classification of Dental Diseases Using Deep Learning Techniques

This paper develops a dental disease image classification system using deep learning. The system will classify five different classes: cavity, dead tooth, gingivitis, cold sores, and healthy teeth. Data sets used in this study are collected from different sources having different images of diseases found in the teeth. This suggested method extracts the information contained within the photos and assigns the classes using a convolutional neural network, CNN. The architecture in this research is based on the VGG-16 model. Transfer learning has also been used to fine-tune the pre-trained model with higher accuracy for better classification results. The system's performance metrics are accuracy, precision, recall, and F1-score. The performance metrics of the system are high; for cavities, gingivitis, and healthy teeth at 100% accuracy, recall, and F1-scores. While the dead tooth classification has an Precision of 100%, with accuracy being 0.89, recall is 0.89, and an F1-score is 0.94.