Exploring Deep Learning Techniques for Data Augmentation in Medical Image Datasets: A Survey

This review paper introduces an advanced deep-learning method to tackle imbalanced medical datasets, frequently arising due to limited data availability and privacy restrictions in healthcare institutions. Implementing a tailored, Progressively Growing Generative Adversarial Network (PGGAN) enhances data augmentation by producing high-quality synthetic X-ray images, providing a valuable solution for medical imaging applications. Combined with transfer learning techniques, it enables adapting standard classification models for improved performance in medical contexts. The approach demonstrates significant potential in enhancing the prediction accuracy of chest pathologies and overcoming data imbalance, paving the way for broader applications in medical diagnostics.