Data-GAN Augmentation Techniques in Medical Image Analysis: A Deep Survey

Generative Adversarial Networks (GANs) have emerged as a powerful tool for data augmentation in medical imaging, enabling the generation of realistic synthetic images to augment small and heterogeneous training datasets. This work emphasizes to introduce the basics of data augmentation and types (both traditional and advanced) then present a detailed review of different GAN-based data augmentation techniques. Here we discuss the advantages and limitations of each technique and summarize the various evaluation metrics used to assess their performance. In recent studies we reviewed recent studies that have used GAN-based data augmentation to improve the performance of deep learning models in various medical imaging applications, including MRI and CT image analysis, retinal image segmentation, and pulmonary nodule detection. Finally, we discuss the current challenges and future research directions in this field, including the need for large-scale evaluation studies and the development of more efficient and effective GAN-based data augmentation methods. This work provides a comprehensive overview of the state-of-the-art in GAN-based data augmentation techniques in medical image analysis and highlights their potential to improve the accuracy and reliability of deep learning models in medical imaging. Based on our observations, this trend will continue, and we therefore conducted a deep review of recent advances in medical imaging using the GAN techniques with a hope of benefiting researchers interested in this technique. Each finding is complimented by a novel table summary and explains how well the GAN models have blended in each of the medical application with increasing use in coming years with correct amalgamation of model, dataset and strategy chosen for the research problem. This work firmly believe that this survey will prove to be a handy summary for all queries researchers ideally look for before choosing the GAN model as their research problem. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.