A Review of COVID-19 Diagnosis and Detection Using Artificial Intelligence

Authors

  • Suhad Hussein Jasim Department of Electrical Engineering, University of Technology, Baghdad, Iraq

DOI:

https://doi.org/10.59746/jfes.v1i1.9

Keywords:

Neural network, Coronavirus, COVID-19

Abstract

Coronavirus has received widespread attention from the community of researchers and
medical scientists in the past year. Deploying based on Artificial Intelligence (AI) networks and
models in real world to learn about and diagnose COVID-19 is a critical mission for medical
personnel to help preventing the rapid spread of this virus. This article is a brief review of recent
papers concerning about detection of the virus; most of the schemes used to detect and diagnose
COVID-19 rely on chest X-Ray, some on sounds of breathing, and by using electrocardiogram (ECG)
trace images, all these schemes based on artificial neural network for early screening of COVID-19
and estimating human mobility to limit its spread. In some studies, an accuracy rate that was obtained
exceeded 95%, which is an acceptable value and that can be relied upon in the diagnosis. Therefore,
currently screening tests are better in terms accuracy and reliability for diagnosing patients with severe
and acute respiratory syndrome coronavirus, frequently the most used test is the (RT-PCR).

References

S. Sani and H. E. Shermeh, “A novel algorithm for detection of COVID-19 by analysis of chest CT images using Hopfield neural network,” Expert Syst. Appl., vol. 197, no. May 2020, p. 116740, 2022, doi: 10.1016/j.eswa.2022.116740.

M. Dahmani et al., “An intelligent and low-cost eye-tracking system for motorized wheelchair control,” Sensors (Switzerland), vol. 20, no. 14, pp. 1–27, 2020, doi: 10.3390/s20143936.

T. He, J. Hu, Y. Song, J. Guo, and Z. Yi, “Multi-task learning for the segmentation of organs at risk with label dependence,” Med. Image Anal., vol. 61, 2020, doi: 10.1016/j.media.2020.101666.

R. Hannan, M. Free, V. Arora, R. Harle, and P. Harvie, “Accuracy of computer navigation in total knee arthroplasty: A prospective computed tomography-based study,” Med. Eng. Phys., vol. 79, pp. 52–59, 2020, doi: 10.1016/j.medengphy.2020.02.003.

B. Pirouz, S. S. Haghshenas, S. S. Haghshenas, and P. Piro, “Investigating a serious challenge in the sustainable development process: Analysis of confirmed cases of COVID-19 (new type of Coronavirus) through a binary classification using artificial intelligence and regression analysis,” Sustain. (United States), vol. 12, no. 6, 2020, doi: 10.3390/su12062427.

A. Hamed, A. Sobhy, and H. Nassar, “Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm,” Arab. J. Sci. Eng., vol. 46, no. 9, pp. 8261–8272, 2021, doi: 10.1007/s13369-020-05212-z.

M. Z. Islam, M. M. Islam, and A. Asraf, “A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images,” Informatics Med. Unlocked, vol. 20, p. 100412, 2020, doi: 10.1016/j.imu.2020.100412.

O. Gozes et al., “Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis,” 2020, [Online]. Available: http://arxiv.org/abs/2003.05037

G. Marques, D. Agarwal, and I. de la Torre Díez, “Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network,” Appl. Soft Comput. J., vol. 96, p. 106691, 2020, doi: 10.1016/j.asoc.2020.106691.

N. Aydin and G. Yurdakul, “Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms,” Appl. Soft Comput. J., vol. 97, p. 106792, 2020, doi: 10.1016/j.asoc.2020.106792.

Gayathri, G.V., Satapathy, S.C. (2020). A Survey on Techniques for Prediction of Asthma. In: Satapathy, S., Bhateja, V., Mohanty, J., Udgata, S. (eds) Smart Intelligent Computing and Applications. Smart Innovation, Systems and Technologies, vol 159. Springer, Singapore. https://doi.org/10.1007/978-981-13-9282-5_72

F. Pegoraro, E. A. Portela Santos, E. de Freitas Rocha Loures, and F. W. Laus, “A hybrid model to support decision making in emergency department management,” Knowledge-Based Syst., vol. 203, p. 106148, 2020, doi: 10.1016/j.knosys.2020.106148.

W. Hariri and A. Narin, “Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review,” Soft Comput., vol. 25, no. 24, pp. 15345–15362, 2021, doi: 10.1007/s00500-021-06137-x.

O. Reyad, “Novel Coronavirus COVID-19 Strike on Arab Countries and Territories: A Situation Report I,” pp. 1–3, 2020, [Online]. Available: http://arxiv.org/abs/2003.09501

H. Liu, F. Liu, J. Li, T. Zhang, D. Wang, and W. Lan, “Clinical and CT imaging features of the COVID-19 pneumonia: Focus on pregnant women and children,” J. Infect., vol. 80, no. 5, pp. e7–e13, 2020, doi: 10.1016/j.jinf.2020.03.007.

M. E. Karar, E. E.-D. Hemdan, and M. A. Shouman, “Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans,” Complex Intell. Syst., vol. 7, no. 1, pp. 235–247, 2021, doi: 10.1007/s40747-020-00199-4.

W. M. Shaban, A. H. Rabie, A. I. Saleh, and M. A. Abo-Elsoud, “Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network,” Appl. Soft Comput., vol. 99, p. 106906, 2021, doi: 10.1016/j.asoc.2020.106906.

J. Laguarta, F. Hueto, and B. Subirana, “COVID-19 Artificial Intelligence Diagnosis Using only Cough Recordings,” IEEE Open J. Eng. Med. Biol., vol. 1, pp. 275–281, 2020, doi: 10.1109/OJEMB.2020.3026928.

K. K. Lella and A. Pja, “Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath,” Alexandria Eng. J., vol. 61, no. 2, pp. 1319–1334, 2022, doi: 10.1016/j.aej.2021.06.024.

T. Rahman et al., “COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network,” Heal. Inf. Sci. Syst., vol. 10, no. 1, pp. 1–17, 2022, doi: 10.1007/s13755-021-00169-1.

B. Xiao et al., “PAM-DenseNet : A Deep Convolutional Neural Network for Computer-Aided COVID-19 Diagnosis,” pp. 1–12, 2021.

P. Melin, D. Sánchez, J. C. Monica, and O. Castillo, Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction, vol. 5, no. 2020. 2021. doi: 10.1007/s00500-020-05549-5.

N. Awasthi, A. Dayal, L. R. Cenkeramaddi, and P. K. Yalavarthy, “Mini-COVIDNet: Efficient Lightweight Deep Neural Network for Ultrasound Based Point-of-Care Detection of COVID-19,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 68, no. 6, pp. 2023–2037, 2021, doi: 10.1109/TUFFC.2021.3068190.

A. Kumar, A. R. Tripathi, S. C. Satapathy, and Y. D. Zhang, “SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network,” Pattern Recognit., vol. 122, p. 108255, 2022, doi: 10.1016/j.patcog.2021.108255.

P. Saha, M. S. Sadi, and M. M. Islam, “EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers,” Informatics Med. Unlocked, vol. 22, p. 100505, 2021, doi: 10.1016/j.imu.2020.100505.

M. Perumal, A. Nayak, R. P. Sree, and M. Srinivas, “INASNET: Automatic identification of coronavirus disease (COVID-19) based on chest X-ray using deep neural network,” ISA Trans., no. xxxx, 2022, doi: 10.1016/j.isatra.2022.02.033.

M. Umer, I. Ashraf, S. Ullah, A. Mehmood, and G. S. Choi, “COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images,” J. Ambient Intell. Humaniz. Comput., vol. 13, no. 1, pp. 535–547, 2022, doi: 10.1007/s12652-021-02917-3.

K. Kc, Z. Yin, M. Wu, and Z. Wu, “Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images,” Signal, Image Video Process., vol. 15, no. 5, pp. 959–966, 2021, doi: 10.1007/s11760-020-01820-2.

Q. Xie et al., “The usage of deep neural network improves distinguishing COVID-19 from other suspected viral pneumonia by clinicians on chest CT: a real-world study,” Eur. Radiol., vol. 31, no. 6, pp. 3864–3873, 2021, doi: 10.1007/s00330-020-07553-7.

A. K. Dash and P. Mohapatra, “A Fine-tuned deep convolutional neural network for chest radiography image classification on COVID-19 cases,” Multimed. Tools Appl., vol. 81, no. 1, pp. 1055–1075, 2022, doi: 10.1007/s11042-021-11388-9.

Downloads

Published

2022-06-01