A novel Pulp Caries GAN multi loss GAN with new pulp inspired metaheuristics for pediatric dental caries detection and segmentation.

ABSTRACT: Early detection of dental caries in pediatric populations remains challenging due to limited annotated datasets and the subtle manifestation of incipient lesions. This study introduces Pulp-Caries-GAN, a novel generative adversarial network incorporating a biomimetic optimization strategy for high-fidelity synthetic dental image generation. The framework integrates a multi-loss architecture combining adversarial, pixel-wise, perceptual, and structural similarity losses with a pulp-inspired metaheuristic function that models neurophysiological dynamics of dental pulp tissue to preserve anatomical coherence. The optimization strategy employs spatially-adaptive regularization through an anatomical masking mechanism that enforces tissue-specific constraints based on diagnostic importance. Expe

SUBMITTER: Elkhatib AA 

PROVIDER: S-EPMC12783627 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature