Deep Learning for COVID-19 Prognosis: A Systematic Review

In the twenty-first century, the novel coronavirus (COVID-19) with its origin in the city of Wuhan has been spreading expeditiously and infecting more than 4.9 million population of the world as of May 19, 2020. As it is inducing serious threat to the global health, it is necessary to develop accurate prediction models and early diagnosis tools of COVID-19 to empower healthcare specialist and government authorities to control the spread of the pandemic. The latest advances in the intelligent computing particularly deep learning approaches are providing a wide range of efficient methods, paradigms and tools in the interpretation and prophecy of COVID-19. In this paper, a perspective research on the ongoing deep learning approaches has been carried out. In this study, an analysis of the different approaches of deep learning techniques in the forecasting, classification and detection of COVID-19 has been performed. The main motive of this research is to facilitate the researchers and technocrats with some critical research briefing that may further assist in developing more adequate prototypes for the analysis and diagnosis of COVID-19.