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

Abstract

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways deep learning techniques are used in this regard and how they potentially further works in combatting the outbreak of COVID-19.

18 pages, 2 figures, 4 Tables

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• Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients
• A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron
• New and emerging forms of data and technologies: literature and bibliometric review
• Supervised and Self-supervised Pretraining Based COVID-19 Detection Using Acoustic Breathing/Cough/Speech Signals
• On the Composition and Limitations of Publicly Available COVID-19 X-Ray Imaging Datasets
• Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images
• COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach
• Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects
• Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19
• Novel applications of Generative Adversarial Networks (GANs) in the analysis of ultrafast electron diffraction (UED) images