In this work, we propose a new gan architecture for augmentation of. These datasets often suffer from severe. This research addresses the imbalance in medical imaging datasets, specifically in chest radiography, by leveraging generative adversarial networks (gans) for data augmentation.
We compare performance to traditional.
This study introduces a progressive. Nevertheless, data augmentation techniques for training gans are underexplored compared to cnns. We compare performance to traditional.
