Diabetic retinopathy (DR) is an ailment affecting the eyes of a diabetic person. It may lead to loss of eyesight if left untreated for a long time. To aid the detection of DR in an early stage, we came up with the idea of applying a semi-supervised Generative Adversarial Network (GAN) for designing an automated diagnostic model. The model, named ‘ForeseeGAN,' is capable of data augmentation and classification as a step for diagnosis, with an accuracy of 95.920%. The automated working capability and promptness mark the excellence of this study. The existing studies were capable of either classification using machine learning or deep learning techniques. However, ForeseeGAN has the capability of data augmentation, thus making it capable of accurate analysis even with fewer data. Our study can be of immense use to the medical community in detecting retinal diseases from the image without a huge dataset.