Generative Adversarial Network-a Comparative Analysis

Machine learning (ML) is a subset of artificial intelligence domain that incorporates a wide range of techniques including Genetic algorithms, Artificial Neural Networks, Bayesian Learning, decision trees. Analytical Learning and other supervised or unsupervised learning models. These techniques exploit present and arriving data to recognize the patterns and foresee forthcoming clinical events. There are number of evolving applications of ML in other aspects of patient management that have not yet been analyzed. Generative Adversarial networks (GANs), a deep neural networks which are capable of generating or transforming images and can support in faster imaging by creatingrealistic images utilizing the current imaging protocols in a variety of contrast and modalities. This paper deals with the big picture analysis of GAN along with architecture, comparison with existing approaches, application areas and the challenges associated with it.