DiffuPT: Class Imbalance Mitigation for Glaucoma Detection via Diffusion Based Generation and Model Pretraining

Glaucoma is a progressive optic neuropathy character-ized by structural damage to the optic nerve head andfunctinoal changes in the visual field. Detecting glaucoma early is crucial to preventing loss of eyesight. However, med-ical datasets often suffer from class imbalances, making detection more difficult for deep-learning algorithms. We use a generative-based framework to enhance glaucoma di-agnosis, specifically addressing class imbalance through synthetic data generation. In addition, we collected the largest national dataset for glaucoma detection to support our study. The imbalance between normal and glaucoma-tous cases leads to performance degradation of classifier models. We created a more robust classifier training process by combining our proposed framework leveraging diffusion models with a pretraining approach. This training process results in a better-performing classifier. The proposed approach shows promising results in improving the harmonic mean “sensitivity and specificity” and AU C for the roc for the glaucoma classifier. We report an improvement in the harmonic mean metric from 89.09% to 92.59% on the test set of our Egyptian dataset. We examine our method against other methods to overcome imbalance through extensive experiments. We report similar improvements on the AIROGS dataset. This study highlights that diffusion-based generatino can be important in tackling class imbalances in medi-cal datasets to improve diagnostic performance.