Data Augmentation with Generative Adversarial Networks for Deep Learning in Healthcare

In order to overcome the difficulties caused by small datasets, this study investigates the combination of Generative Adversarial Networks (GANs) for healthcare information augmentation. Using a descriptive method with additional data gathering, we apply an approach based on a deductive and an interpretivist framework. The findings include a robustness test, a visual assessment, a comparative efficiency study, and an objective evaluation. Technical nuances and ethical implications are highlighted in our critical study. Suggestions support improving GAN structures, verifying robustness, and fostering responsible implementation. Subsequent research ought to concentrate on customized GANs for particular medical modalities, moral principles, and real-time therapeutic implementations.