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

TL;DR

This review paper discusses recent deep learning methods for diagnosing COVID-19 using medical imaging, analyzing datasets, techniques, and challenges to guide future research in this critical area.

Contribution

It provides a comprehensive overview of deep learning approaches for COVID-19 diagnosis, categorizing recent works and highlighting challenges and future directions.

Findings

Deep learning models show promising accuracy in COVID-19 detection.

Various datasets and data partitioning techniques are used for training.

Challenges include data scarcity and model generalization issues.

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.