Progressive GAN Framework for Realistic Chest X-Ray Synthesis and Data Augmentation

The scarcity of large-scale labeled medical imaging datasets presents a significant bottleneck for advancing diag-nostic model performance in radiology. This study introduces a progressive Generative Adversarial Network (GAN) framework for synthesizing high-quality, anatomically consistent chest x-ray images. The proposed model leverages a generator with progressive upsampling and residual connections to preserve fine-grained anatomical details and a PatchGAN-based discriminator to ensure local and global realism. A comprehensive preprocessing pipeline, including contrast enhancement, morphological operations, Fourier analysis, and data augmentation, was employed to prepare high-quality input data. Quantitative evaluation metrics such as Frechet Inception Distance (FID) and Structural Similarity Index (SSIM) demonstrated the model's effectiveness, achieving low FID scores and high SSIM values, indicative of the synthetic images' similarity to real chest X-rays. Qualitative analyses further validated the generated images' clinical relevance and visual fidelity, highlighting their potential for augmenting medical imaging datasets and supporting downstream diagnostic tasks.