Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review

Overview

Journal Neural Process Lett

Date 2022 Sep 26

PMID 36158520

Authors

Yogesh H Bhosale

K Sridhar Patnaik

Affiliations

Soon will be listed here.

Abstract

Covid-19 is now one of the most incredibly intense and severe illnesses of the twentieth century. Covid-19 has already endangered the lives of millions of people worldwide due to its acute pulmonary effects. Image-based diagnostic techniques like X-ray, CT, and ultrasound are commonly employed to get a quick and reliable clinical condition. Covid-19 identification out of such clinical scans is exceedingly time-consuming, labor-intensive, and susceptible to silly intervention. As a result, radiography imaging approaches using Deep Learning (DL) are consistently employed to achieve great results. Various artificial intelligence-based systems have been developed for the early prediction of coronavirus using radiography pictures. Specific DL methods such as CNN and RNN noticeably extract extremely critical characteristics, primarily in diagnostic imaging. Recent coronavirus studies have used these techniques to utilize radiography image scans significantly. The disease, as well as the present pandemic, was studied using public and private data. A total of 64 pre-trained and custom DL models concerning imaging modality as taxonomies are selected from the studied articles. The constraints relevant to DL-based techniques are the sample selection, network architecture, training with minimal annotated database, and security issues. This includes evaluating causal agents, pathophysiology, immunological reactions, and epidemiological illness. DL-based Covid-19 detection systems are the key focus of this review article. Covid-19 work is intended to be accelerated as a result of this study.

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References

Wu X, Hui H, Niu M, Li L, Wang L, He B . Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study. Eur J Radiol. 2020; 128:109041. PMC: 7198437. DOI: 10.1016/j.ejrad.2020.109041. View

Gilanie G, Bajwa U, Waraich M, Asghar M, Kousar R, Kashif A . Coronavirus (COVID-19) detection from chest radiology images using convolutional neural networks. Biomed Signal Process Control. 2021; 66:102490. PMC: 7874961. DOI: 10.1016/j.bspc.2021.102490. View

Ozturk T, Talo M, Yildirim E, Baloglu U, Yildirim O, Rajendra Acharya U . Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med. 2020; 121:103792. PMC: 7187882. DOI: 10.1016/j.compbiomed.2020.103792. View

Ucar F, Korkmaz D . COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses. 2020; 140:109761. PMC: 7179515. DOI: 10.1016/j.mehy.2020.109761. View

Kumar A, Tripathi A, Satapathy S, Zhang Y . SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network. Pattern Recognit. 2021; 122:108255. PMC: 8386119. DOI: 10.1016/j.patcog.2021.108255. View

Jiang H, Tang S, Liu W, Zhang Y . Deep learning for COVID-19 chest CT (computed tomography) image analysis: A lesson from lung cancer. Comput Struct Biotechnol J. 2021; 19:1391-1399. PMC: 7923948. DOI: 10.1016/j.csbj.2021.02.016. View

Shakouri S, Bakhshali M, Layegh P, Kiani B, Masoumi F, Ataei Nakhaei S . COVID19-CT-dataset: an open-access chest CT image repository of 1000+ patients with confirmed COVID-19 diagnosis. BMC Res Notes. 2021; 14(1):178. PMC: 8114670. DOI: 10.1186/s13104-021-05592-x. View

Song Y, Zheng S, Li L, Zhang X, Zhang X, Huang Z . Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images. IEEE/ACM Trans Comput Biol Bioinform. 2021; 18(6):2775-2780. PMC: 8851430. DOI: 10.1109/TCBB.2021.3065361. View

Modi S, Guhathakurta R, Praveen S, Tyagi S, Bansod S . Detail-Oriented Capsule Network for classification of CT scan images performing the detection of COVID-19. Mater Today Proc. 2021; 80:3709-3713. PMC: 8295010. DOI: 10.1016/j.matpr.2021.07.367. View

10.

Javor D, Kaplan H, Kaplan A, Puchner S, Krestan C, Baltzer P . Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography. Eur J Radiol. 2020; 133:109402. PMC: 7641539. DOI: 10.1016/j.ejrad.2020.109402. View

11.

Perumal V, Narayanan V, Sundar Rajasekar S . Detection of COVID-19 using CXR and CT images using Transfer Learning and Haralick features. Appl Intell (Dordr). 2022; 51(1):341-358. PMC: 8852781. DOI: 10.1007/s10489-020-01831-z. View

12.

Tiwari S, Jain A . A lightweight capsule network architecture for detection of COVID-19 from lung CT scans. Int J Imaging Syst Technol. 2022; 32(2):419-434. PMC: 9015631. DOI: 10.1002/ima.22706. View

13.

Al-Waisy A, Al-Fahdawi S, Mohammed M, Abdulkareem K, Mostafa S, Maashi M . COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images. Soft comput. 2020; 27(5):2657-2672. PMC: 7679792. DOI: 10.1007/s00500-020-05424-3. View

14.

S Alghamdi H, Amoudi G, Elhag S, Saeedi K, Nasser J . Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey. IEEE Access. 2021; 9:20235-20254. PMC: 8545235. DOI: 10.1109/ACCESS.2021.3054484. View

15.

Vaid S, Kalantar R, Bhandari M . Deep learning COVID-19 detection bias: accuracy through artificial intelligence. Int Orthop. 2020; 44(8):1539-1542. PMC: 7251557. DOI: 10.1007/s00264-020-04609-7. View

16.

Kedia P, Anjum , Katarya R . CoVNet-19: A Deep Learning model for the detection and analysis of COVID-19 patients. Appl Soft Comput. 2021; 104:107184. PMC: 7883765. DOI: 10.1016/j.asoc.2021.107184. View

17.

Apostolopoulos I, Mpesiana T . Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020; 43(2):635-640. PMC: 7118364. DOI: 10.1007/s13246-020-00865-4. View

18.

Yang S, Jiang L, Cao Z, Wang L, Cao J, Feng R . Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study. Ann Transl Med. 2020; 8(7):450. PMC: 7210135. DOI: 10.21037/atm.2020.03.132. View

19.

Tang S, Wang C, Nie J, Kumar N, Zhang Y, Xiong Z . EDL-COVID: Ensemble Deep Learning for COVID-19 Case Detection From Chest X-Ray Images. IEEE Trans Industr Inform. 2023; 17(9):6539-6549. PMC: 8545018. DOI: 10.1109/TII.2021.3057683. View

20.

Elkorany A, Elsharkawy Z . COVIDetection-Net: A tailored COVID-19 detection from chest radiography images using deep learning. Optik (Stuttg). 2021; 231:166405. PMC: 7848537. DOI: 10.1016/j.ijleo.2021.166405. View