Medical Image Synthesis using Generative Adversarial Network

Abstract

Medical image synthesis has emerged as a promising technique in the field of healthcare, enabling the generation of realistic medical images for various applica tions. This study focuses on medical image synthesis using Generative Adversarial Networks (GANs) applied to the IDRID dataset, which contains retinal images for diabetic retinopathy analysis. The objective of this research is to explore the potential of GANs in generating synthetic retinal images that closely resemble real patient data. The IDRID dataset provides a valuable resource for training and evaluating the GAN model. By leveraging the power of GANs, the proposed framework aims to generate high-quality synthetic retinal images with similar char acteristics and visual appearance to real patient images. This has the potential to augment the existing dataset, expand its diversity, and improve the performance of diagnostic and treatment algorithms. The methodology involves training a GAN architecture consisting of a generator and a discriminator network. The generator network learns to generate synthetic retinal images from random noise, while the discriminator network evaluates the authenticity of the generated images. The two networks engage in an adversarial training process, where the generator aims to fool the discriminator into classifying the synthetic images as real. Evaluation of the synthesized retinal images includes quantitative metrics such as structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and analysis to as sess the similarity and quality of the generated images compared to real IDRID dataset images. The outcomes of this research provide insights into the capabili ties of GANs in generating realistic retinal images from the IDRID dataset. The generated images have the potential to enhance the limited availability of labeled medical data, facilitate algorithm development, and support computer-aided di agnosis systems. The findings contribute to the broader field of medical image synthesis, showcasing the potential of GANs in improving healthcare outcomes through enhanced image data availability and diversity