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A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

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Document pages: 18 pages

Abstract: Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation allover the world and has become one of the most acute and severe ailments in thepast hundred years. The prevalence rate of COVID-19 is rapidly rising every daythroughout the globe. Although no vaccines for this pandemic have beendiscovered yet, deep learning techniques proved themselves to be a powerfultool in the arsenal used by clinicians for the automatic diagnosis of COVID-19.This paper aims to overview the recently developed systems based on deeplearning techniques using different medical imaging modalities like ComputerTomography (CT) and X-ray. This review specifically discusses the systemsdeveloped for COVID-19 diagnosis using deep learning techniques and providesinsights on well-known data sets used to train these networks. It alsohighlights the data partitioning techniques and various performance measuresdeveloped by researchers in this field. A taxonomy is drawn to categorize therecent works for proper insight. Finally, we conclude by addressing thechallenges associated with the use of deep learning methods for COVID-19detection and probable future trends in this research area. This paper isintended to provide experts (medical or otherwise) and technicians with newinsights into the ways deep learning techniques are used in this regard and howthey potentially further works in combatting the outbreak of COVID-19.

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