A Hybrid GAN-Diffusion Frameworks for data Argumentation in brain Tumor and Chest X-Ray Classification : A Review

Author(s)	Ms. MADHURI PANDIT PUJARI, Prof. PRASAD BHOSALE, Prof. SHAMINABANO SHAIKH, Prof. VISHAL SHINDE
Country	India
Abstract	Data insufficiency and class imbalance are still significant issues in medical image classification tasks, especially in brain tumor magnetic resonance imaging (MRI) and chest X-ray image analysis. However, generative data augmentation has been identified as an effective technique to increase the diversity of the dataset and improve the performance of deep learning models. This review provides a thorough examination of Generative Adversarial Networks (GANs), particularly DCGAN-based methods, and diffusion models for medical image generation. The latest developments in GAN-based augmentation for brain MRI and chest X-ray image classification tasks and diffusion based generative models are critically discussed. In addition, the potential of hybrid generative approaches that combine GAN and diffusion models to improve the diversity and realism of generated data is also examined. This paper reveals the research gaps in the current studies and proposes a conceptual framework of a hybrid GAN and diffusion model for medical image classification.
Keywords	GAN, Brain MRI, DCGAN, Diffusion Model, Chest X-Ray, Data Augmentation, Medical Image classification
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-09

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International Journal For Multidisciplinary Research