Deep Learning-Based Detection and Classification of Dental Conditions

Dental conditions, particularly calculus and caries, are among the primary causes of poor oral health. They frequently result in pain, discomfort, infections, and occasionally even tooth loss. To prevent these problems from getting worse, early detection is vital. In many places, though, access to dental specialists is still restricted. In order to automatically identify and categorize four crucial aspects of oral health—caries, calculus, discoloration, and healthy teeth—this study proposes a deep learning approach. By using readily available intraoral images, the model removes the need for radiographic methods up to an extent, allowing for affordable and non-invasive screening. The system uses Transfer Learning with a ResNet-based Convolutional Neural Network (CNN), which provides excellent feature extraction and quick learning even with smaller datasets. We trained the model extensively with pre-processed intraoral images and fine-tuned the higher convolutional layers. In tests on the validation dataset, the model reached a classification accuracy of 97.35 percent. This shows it can accurately tell the difference between healthy and diseased dental states with great precision and low variance 0.34 percent across the cross-validation tests. We can expand the community's access to early detection through integrating them into applications. This framework marks a major breakthrough in the use of deep learning models in preventive dentistry with its rapid, scalable, and accurate screening that improves patient care and public health outcomes.