GAN-DCNN: A Generative Adversial Network based Deep Convolutional Neural Network for Retinal Vessel Enhancement

Abstract Artificial intelligence is taking over the technology transmission in all areas. Generative AI is now mostly used in the healthcare sector due to its vivid application area. The emergence of Generative Adversial Network (GAN) in medical domain is highly recommended. The proposed work brings the essence of deep convolutional network that differentiates real and generated retinal images. This work highlights the advancement of GAN over other artificial intelligence models. By identifying appropriate networks of GAN, retinal images are generated in high quality. Low resolution images are enhanced with GAN models. The proposed work involves basic preprocessing, augmentation and enhancement with the retinal images that provide better accuracy with epochs. Deep learning models with GAN implementation are highly preferred for the retinal classification.