  The Coronavirus (COVID-19) outbreak in December 2019 has become an ongoing
threat to humans worldwide, creating a health crisis that infected millions of
lives, as well as devastating the global economy. Deep learning (DL) techniques
have proved helpful in analysis and delineation of infectious regions in
radiological images in a timely manner. This paper makes an in-depth survey of
DL techniques and draws a taxonomy based on diagnostic strategies and learning
approaches. DL techniques are systematically categorized into classification,
segmentation, and multi-stage approaches for COVID-19 diagnosis at image and
region level analysis. Each category includes pre-trained and custom-made
Convolutional Neural Network architectures for detecting COVID-19 infection in
radiographic imaging modalities; X-Ray, and Computer Tomography (CT).
Furthermore, a discussion is made on challenges in developing diagnostic
techniques such as cross-platform interoperability and examining imaging
modality. Similarly, a review of the various methodologies and performance
measures used in these techniques is also presented. This survey provides an
insight into the promising areas of research in DL for analyzing radiographic
images, and further accelerates the research in designing customized DL based
diagnostic tools for effectively dealing with new variants of COVID-19 and
emerging challenges.
