Generating Realistic X-ray Images Using GANs

Modern developments in the medical industry have been significantly accelerated by the promising advancements of Generative Adversarial Networks (GANs). This study introduces an intriguing exploration into the utilization of Deep Convolutional Generative Adversarial Networks (DCGANs). Using GANs’ inherent capabilities, our objective is to build high-fidelity pictures with detailed anatomical features and qualities similar to actual X-ray images. The proposed methodology of this work proves effective in addressing the challenges posed by limited medical imaging data. It generates diverse and expansive datasets, which can enhance machine learning models in medical research and diagnostics. The study presents a novel approach to create synthetic X-ray images that closely resemble original CXR images, with the potential to significantly contribute to medical imaging. Consequently, our research not only advances the application of GANs in medical imaging but also holds promise in shaping the future landscape of healthcare technology.