A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron

ABSTRACT

The Coronavirus (COVID-19) outbreak in December 2019 has drastically affected humans worldwide, creating a health crisis that has infected millions of lives and devastated the global economy. COVID-19 is ongoing, with the emergence of many new strains. Deep learning (DL) techniques have proven helpful in efficiently analysing and delineating infectious regions in radiological images. This survey paper draws a taxonomy of deep learning techniques for detecting COVID-19 infection in radiographic imaging modalities Chest X-Ray, and Computer Tomography. DL techniques are broadly categorised into classification, segmentation, and multi-stage approaches for COVID-19 diagnosis at the image and region-level analysis. These techniques are further classified as pre-trained and custom-made Convolutional Neural Network architectures. Furthermore, a discussion is drawn on radiographic datasets, evaluation metrics, and commercial platforms provided for detection. In the end, a brief look is paid to emerging ideas, gaps in existing research, and challenges in developing diagnostic techniques. This survey provides insight into the promising areas of research in DL and is likely to guide the research community on the upcoming development of deep learning techniques for COVID-19. This will pave the way to accelerate the research in designing customised DL-based diagnostic tools for effectively dealing with new variants of COVID-19 and emerging challenges.

KEYWORDS:

• COVID-19
• Omicron
• deep learning
• chest X-ray

Acknowledgment

We thank Pattern Recognition Lab (PR-Lab), Pakistan Institute of Engineering and Applied Sciences (PIEAS) for providing necessary computational resources and a healthy research environment. Moreover, we were also supported by the Department of Computer Systems Engineering, University of Engineering and Applied Science (UEAS).

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.