The Role of Fundus Imaging and GAN in Diabetic Retinopathy Classification using VGG19

Diabetes is a dangerous medical condition that have to be identified at early stages if not might produce adverse consequences later. The fundus image of the retina can be effective in understanding the diabetic relevancy and can also produce various other results related to retinal diseases using angiography techniques. This involves injecting dye, which has severe side effects like nausea, severe allergic reactions, vomiting and might turn even worse. However, these side effects can be overcome by expensive techniques that are not affordable for many. To solve this issue, a combination of GAN or Generative Adversarial Networks with VGG can be effective in classifying whether the retina scan input image is affected by diabetes or not. The involvement of GAN with a classification algorithm is entertained due to the imbalance of medical records and the complexity involved with extensive training with CNN, which produces pessimistic results due to the lower number of records. GANs offer a solution to the challenge of sparse data in generating synthetic retinal images. Conversely, VGG stands out as a deep convolutional neural network ideal for image classification tasks. By combining these two techniques, it is possible to improve the accuracy of diabetic retinopathy detection. This approach has the potential to make diabetic retinopathy screening more accessible and affordable, especially in low-resource settings where traditional methods may not be feasible.