Generative Adversarial Networks for Data Augmentation in Medical Imaging

Manoj Chowdary Vattikuti

Abstract


The scarcity of labeled medical imaging data poses challenges for training robust AI models. This paper explores the use of Generative Adversarial Networks (GANs) for data augmentation in medical imaging. The proposed framework generates synthetic images that closely resemble real medical scans, enhancing the diversity of training datasets. Experiments on datasets for diseases such as cancer and lung conditions demonstrate that incorporating GAN-augmented data significantly improves model performance in classification and segmentation tasks. This study highlights the potential of GANs to address data limitations in medical AI applications.

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