Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review

Abstract

COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions. This study consists of three stages: planning, conduction, and analysis/reporting. In the conduction stage, inclusion and exclusion criteria are applied to the literature searching and identification. Then, we have implemented quality assessment rules, where over 75 scored articles in the literature were included. Finally, in the analysis/reporting stage, all the papers are reviewed and analysed. After the quality assessment of the individual papers, this study adopted 57 articles for the systematic literature review. From these reviews, the critical analysis of each paper, including the represented matrix for the model evaluation, existing contributions, and motivation, has been tracked with suitable illustrations. We have also interpreted several insights of each paper with appropriate annotation. Further, a set of comparisons has been enumerated with suitable discussion. Convolutional neural networks are the most commonly used deep learning architecture for COVID-19 disease classification and identification from X-ray and CT images. Various prior studies did not include data from a hospital setting nor did they consider data preprocessing before training a deep learning model.

Keywords: Computed tomography (CT) images; Coronavirus (COVID-19); Deep learning (DL); Machine learning (ML); RT-PCR; X-ray images.

Fig. 1

Overall project methodology

Fig. 2

Systematic literature search and selection flowchart

Fig. 3

COVID-19 research around the globe

Fig. 4

Deep learning models for detecting the COVID-19 patients

Fig. 5

Accuracy of the models and the study

Fig. 6

Variation in highest accuracy of the models

Fig. 7

The occurrence of models that worked on X-ray images

Fig. 8

The occurrence of models that worked on CT images

Fig. 9

Diagnosis of COVID-19 using deep learning

Fig. 10

Variation in highest accuracy of the models

Fig. 11

The occurrence of models that worked on other images

Fig. 12

Variation in highest accuracy of the models

Fig. 13

The occurrence of models in recent articles