Integrating DCGANs for Dental Image Augmentation

Integration of artificial intelligence (AI) and machine learning (ML) technologies has greatly affected developments in dental healthcare recently. This paper addresses the enhancement of dental radiography images using Deep Convolutional Generative Adversarial Networks (DCGANs) the goal is to improve the variety and amount of training data. The suggested approach, dataset properties, and DCGAN implementation procedure in the dental imaging environment are described in this work. The findings show significant increases in model accuracy and robustness, suggesting the possibility of DCGANs transforming dental diagnosis and contribute to the Sustainable Development Goals.