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Artificial Neural Network is a domain where an abundant amount of data is needed to train the neural network model to predict the output precisely. But in a real scenario, there are many application areas like astronomy, clinical dataset, rare species identification, financial datasets, and satellite images where data are scarce the network is unable to predict unusual classes of events. The environment in which we live is uncertain, unpredictable and in the future, we may face more challenges. To overcome this, we have the most predominant image and video analysis technique called Generative Adversarial Neural Network (GAN) which is very useful for any sparse datasets especially in pandemic situations like COVID-19 and also for any future events for which we don't have enough datasets to train the model. This paper aims to overview the details about the systems recently developed to diagnose novel COVID-19 with the help of X-ray and CT-scan images collected from different infected persons using one of the most important branches of Deep learning technique known as DCGAN(Deep Convolutional Generative Adversarial Neural Network) for detecting various new emerging diseases in the medical field for which dataset is very less and DCGAN acts as an efficient tool for generating synthetic data as there is scarcity in the availability of data for diagnosing coronavirus. İt also describes the datasets taken from various publicly available sources so that a large number of image datasets can be trained and tested in various state-of-art deep network architectures to check the performance and efficiency of each model in terms of accuracy, complexity, and time of execution. These results help radiologists to quickly identify the disease without any further delay in spreading and infecting others.

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