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. 2021 Mar 15:2021:6677314.
doi: 10.1155/2021/6677314. eCollection 2021.

Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review

Mustafa Ghaderzadeh  1 , Farkhondeh Asadi  2
Affiliations

Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review

Mustafa Ghaderzadeh et al. J Healthc Eng. .

Erratum in

Abstract

Introduction: The early detection and diagnosis of COVID-19 and the accurate separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease are among the main challenges in the current COVID-19 pandemic. Concerning the novelty of the disease, diagnostic methods based on radiological images suffer from shortcomings despite their many applications in diagnostic centers. Accordingly, medical and computer researchers tend to use machine-learning models to analyze radiology images. Material and Methods. The present systematic review was conducted by searching the three databases of PubMed, Scopus, and Web of Science from November 1, 2019, to July 20, 2020, based on a search strategy. A total of 168 articles were extracted and, by applying the inclusion and exclusion criteria, 37 articles were selected as the research population.

Result: This review study provides an overview of the current state of all models for the detection and diagnosis of COVID-19 through radiology modalities and their processing based on deep learning. According to the findings, deep learning-based models have an extraordinary capacity to offer an accurate and efficient system for the detection and diagnosis of COVID-19, the use of which in the processing of modalities would lead to a significant increase in sensitivity and specificity values.

Conclusion: The application of deep learning in the field of COVID-19 radiologic image processing reduces false-positive and negative errors in the detection and diagnosis of this disease and offers a unique opportunity to provide fast, cheap, and safe diagnostic services to patients.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
PRISMA flow diagram of the review process and exclusion of papers.
Figure 2
Figure 2
Aim of studies in processing of COVID-19 radiology modalities by means of DL.
Figure 3
Figure 3
Rate of using different radiological modalities in processing of COVID-19 by means of DL.
Figure 4
Figure 4
Rate of CNN architectures used in the analysis of radiology modality images of COVID-19.
See this image and copyright information in PMC

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