1 Introduction

2 Overview of Radiographic Data and its Characteristics
2.1 X-Ray Characteristics
2.2 CT Characteristics

3 COVID-19 Diagnostic Strategies
3.1 Image Level Diagnosis
3.2 Region Level Diagnosis

4 Taxonomy of DL Techniques for COVID-19 Diagnosis
4.1 Classification Techniques
4.2 Segmentation Techniques
4.3 Multi-stage Techniques
4.4 Transfer Learning
4.5 Custom-made Architecture

5 Dataset and Performance Metrics

6 Analysis of Existing Literature
6.1 Analysis of Classification Techniques
6.2 Analysis of Segmentation Techniques
6.3 Analysis of Multi-Stage Techniques

7 DL-based Diagnostic Applications for COVID-19 Detection

8 Challenges
8.1 Unavailability of Standard Radiographic Dataset
8.2 Variation in the Visual Quality of Images
8.3 High Class Imbalance
8.4 High Computational Cost and Limited Resources
8.5 Optimization of DL Methods
8.6 Cross-Validation Strategy for Model Evaluation
8.7 Generalization of DL Models
8.8 Translation of Deep Learning Techniques into Clinical Setup
8.9 Radiologists’ Reservations on CAD System
8.10 Lack of Radiological Interpretation in DL Techniques
8.11 Lack of Interoperability Standards

9 Research Gap and Future Direction
Architectural Ideas for Detecting Omicron

10 Conclusion