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Review
. 2022 May:144:105350.
doi: 10.1016/j.compbiomed.2022.105350. Epub 2022 Mar 3.

COVID-19 image classification using deep learning: Advances, challenges and opportunities

Priya Aggarwal  1 , Narendra Kumar Mishra  2 , Binish Fatimah  3 , Pushpendra Singh  4 , Anubha Gupta  5 , Shiv Dutt Joshi  6
Affiliations
Review

COVID-19 image classification using deep learning: Advances, challenges and opportunities

Priya Aggarwal et al. Comput Biol Med. 2022 May.

Abstract

Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.

Keywords: COVID-19 detection; Convolutional neural networks; Deep learning; X-ray and CT scan Images.

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Conflict of interest 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.

Figures

Fig. 1
Fig. 1
Most common classes considered for labelling of CXR and CT-scan images, where SARS stands for Severe Acute Respiratory Syndrome and MERS stands for Middle East Respiratory Syndrome.
Fig. 2
Fig. 2
CXR images of (2a) a COVID-19, (2b) a bacterial pneumonia, (2c) a viral pneumonia, and (2d) a healthy subject.
Fig. 3
Fig. 3
CT-scan images of (3a) a COVID-19 and (3b) a healthy subject.
Fig. 4
Fig. 4
A work flow of Deep learning based COVID-19 detection pipeline.
Fig. 5
Fig. 5
Schematic representation of a typical Convolutional Neural Network architecture.
Fig. 6
Fig. 6
Schematic representation of Transfer Learning approach.
Fig. 7
Fig. 7
(7a) shows the number of publications using most popular datasets for validating COVID-19 detection models, and (7b) shows the number of published papers using various deep learning architectures.
See this image and copyright information in PMC

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