Dental Image Processing for Cavity Detection and Restoration Planning Using GAN

Abstract

Dental image processing plays a critical role in modern dentistry, enabling precise cavity
detection and effective restoration planning. This study explores the use of Generative
Adversarial Networks (GANs) for enhancing the diagnostic process and improving the analysis
of dental radiographs and intraoral images. GANs are employed for tasks such as image
enhancement, segmentation, and anomaly detection, focusing on cavities and associated dental
conditions.The proposed framework integrates GANs to refine image quality, making low-
resolution or noisy dental images suitable for detailed analysis. A segmentation module,
powered by GAN-based architectures, accurately identifies cavities, demarcates affected
regions, and highlights structural details essential for restoration planning. Additionally, the
system incorporates predictive modeling to suggest optimal restoration strategies, considering
cavity depth, position, and surrounding anatomical structures.Experimental results demonstrate
the efficiency of GANs in improving detection accuracy compared to traditional methods. The
system also reduces diagnostic time, assisting dentists in formulating precise, patient-specific
treatment plans. This approach showcases the potential of deep learning technologies in
advancing dental healthcare and promoting more reliable, automated diagnostic tools.