Deep Learning-based Synthetic Brain MRI Generation for Enhanced Brain Tumor Detection

Magnetic Resonance Imaging (MRI) is a cornerstone in diagnosing brain tumors, yet the scarcity of tumor-positive cases in datasets hampers machine learning model performance. This paper presents a Deep Convolutional Generative Adversarial Network (DC-GAN) framework to generate high-quality synthetic brain MRI images, addressing class imbalance and improving diagnostic accuracy. This methodology includes data preprocessing, DC-GAN architecture design, and rigorous evaluation using statistical metrics (SSIM, FID). Results demonstrate that synthetic images exhibit realistic structural features, enhancing tumor detection models. The study underlines the clinical significance of synthetic data and proposes future directions, including 3D GANs and domain adaptation.