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Review
. 2022 Oct 12:2022:1306664.
doi: 10.1155/2022/1306664. eCollection 2022.

Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey

Deepak Sinwar  1 , Vijaypal Singh Dhaka  1 , Biniyam Alemu Tesfaye  2 , Ghanshyam Raghuwanshi  1 , Ashish Kumar  3 , Sunil Kr Maakar  4 , Sanjay Agrawal  5
Affiliations
Review

Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey

Deepak Sinwar et al. Contrast Media Mol Imaging. .

Abstract

Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic.

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Conflict of interest statement

The authors declare that they have no conflicts of interest to report regarding this study.

Figures

Figure 1
Figure 1
(a) Sample X-ray images of COVID-19-affected lungs [2]. (b) Sample CT scan images of COVID-19 patients [3].
Figure 2
Figure 2
Relationship between NN, DL, ML, and AI.
Figure 3
Figure 3
DeepMind's protein structure prediction system [12].
Figure 4
Figure 4
The overall working of the AI4COVID-19 app [15] for distinguishing between a COVID cough and a non-COVID cough.
Figure 5
Figure 5
Sample confusion matrix.
Figure 6
Figure 6
The pipeline of operations performed in DeCoVNet [24].
Figure 7
Figure 7
Summary of COVID-19 image classification model [54].
Figure 8
Figure 8
Automatic identification of COVID-19 from chest X-ray images using DarkCovidNet [56].
Figure 9
Figure 9
Identification of COVID-19 from chest X-ray images using three existing models [58].
Figure 10
Figure 10
Classification of COVID-19 datasets.
See this image and copyright information in PMC

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