Dental Diagnostics with Deep Learning: A VGG16-Based Approach for Classifying Segmented Dental Radiographs

This research focuses on creating a deep learning architecture to categorise dental radiographs into categories such as Cavity, Fillings, Impacted Tooth, Implant, and Normal. Considerable preprocessing was carried out on the Dental Radiography dataset sourced from Kaggle. The dataset was pre-processed by segmenting individual teeth to allow the examination of specific dental conditions. The underlying architecture is a VGG16 convolutional neural network that has availed transfer learning in order to enhance the feature extraction capability. The dataset was divided into training, validation, and test sets, ensuring sufficient representation across all classes. In such a situation, it was able to obtain an overall accuracy of 96.52% on the test set with most classes having high precision and recall along with F1-score. Key findings give evidence of the effectiveness of deep learning for automation of diagnosis from radiographic images, while the VGG16 architecture is found particularly effective. Consequences implied by this research are that AI models can contribute a lot to dental professionals by making preliminary diagnoses faster and more accurately, hence improving diagnostic efficiency and patient care. This study thus holds importance in the sense that this will be one contributing factor to the emergent field of AI-assisted dental diagnostics, overcoming class imbalance problems and showing a possible clinical integration of deep learning models for better diagnosis.