Generative X-Ray Synthesis for Enhanced Disease Prediction in Medical Imaging

Medical imaging plays a pivotal role in disease diagnosis and prediction, where X-ray imaging stands out as a widely employed modality due to its accessibility and efficacy. However, the accuracy of disease prediction in X-ray images heavily relies on the expertise of radiologists and the quality of available data. In this study, we propose a novel approach leveraging generative adversarial networks (GANs) for synthesizing realistic X-ray images to enhance disease prediction capabilities. The proposed Generative X-ray Synthesis (GXS) framework employs GANs to generate synthetic X-ray images with diverse pathological manifestations. By training the GAN on a large dataset of annotated X-ray images, the model learns the complex distribution of anatomical structures and disease patterns. Through iterative training, the generator network progressively improves its ability to synthesize high-fidelity X-ray images that closely resemble real-world cases, Furthermore, we employ transfer learning techniques to fine-tune pre-trained deep learning architectures on the augmented dataset, achieving superior performance in disease prediction tasks compared to models trained solely on real X-ray images. Our experimental results showcase the potential of generative X-ray synthesis as a powerful tool for enhancing disease prediction in medical imaging, ultimately contributing to more accurate and efficient healthcare diagnostics.