Ret-GAN: Retinal Image Enhancement using Generative Adversarial Networks

KC Santosh, Sourodip Ghosh, Moinak Bose

Abstract: With over 200K cases in the U.S. alone, retinal disorders are the most common cause of irreversible blindness. This serves as a primary aim to analyze automated screening tools to detect retinal disorders. We analyze the OCT dataset (84, 484 images) and enhance the images by using Generative Adversarial Networks (GANs). This work specifically focuses on enhancing the quality of source (training) images for better algorithm validatiorr/testing 1 1 Authors contributed equally to the work.. We synthesize super resolution-based images using generators, discriminators and the adversarial nature of the GANs. The performance of the Ret-GAN is validated by PSNR, SSIM, and loss functions. To test the Ret-GAN generated images, we train a convolutional neural network (CNN) with the original dataset images and super-resolution images. We achieve an accuracy of 0.9825 on Ret-GAN generated image data, and 0.9525 on the original data. We statistically analyze the CNN with a number of evaluation metrics to further validate the results. The proposed scheme is compared to benchmark research findings on the same dataset. Our results are encouraging.