Generative Adversarial Networks (GANs) have emerged as a transformative framework in medical imaging, particularly in the generation of high-fidelity synthetic data for diagnostic augmentation. This study explores the application of GANs in producing synthetic tumor imaging data to improve diagnostic accuracy, model generalization, and data balance across medical datasets. Traditional deep learning models often suffer from insufficient annotated data, privacy constraints, and class imbalance limitations that severely impact tumor detection efficiency. By leveraging conditional and cycle-consistent GAN architectures, this research demonstrates the synthesis of anatomically realistic magnetic resonance (MRI) and computed tomography (CT) images that maintain diagnostic relevance and structural consistency. The generated images were validated using quantitative metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Fréchet Inception Distance (FID). Experimental evaluation indicates that synthetic data generated via GANs can enhance the accuracy of tumor classification models by over 8–12% compared to baseline CNN models trained on limited datasets. This study highlights the potential of GAN-based synthetic imaging as a reliable, ethical, and scalable solution for medical data augmentation and clinical model training, paving the way for improved precision in automated tumor diagnostics..
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