AUBADE-syn: a novel deep learning ensemble method for glaucoma detection using synthetic fundus images on imbalanced datasets

Glaucoma is the leading cause of irreversible blindness worldwide. Early detection is essential to preserve vision. Deep learning approaches have shown promise in automating glaucoma detection. However, significant class imbalance in medical datasets often impairs classifier performance. To address this challenge, we propose AUBADE-syn, a deep learning ensemble framework that integrates synthetic image generation with structured class-balancing strategies. Our approach leverages optic nerve head-centered regions and a classifier-free guided…