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Detection of COVID-19 Enhanced by a Deep Extreme Learning Machine

Aaqib Inam1,*, Zhuli1, Ayesha Sarwar1, Salah-ud-din2, Ayesha Atta3, Iftikhar Naaseer4, Shahan Yamin Siddiqui5,6, Muhammad Adnan Khan7

1 School of Software, Institute of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
2 Department of Automation Science and Technology (System Engineering Institute), School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
3 Department of Computer Science, Government College University, Lahore, 54000, Pakistan
4 Department of Computer Science & Information Technology, Superior University, Lahore, 54000, Pakistan
5 School of Computer Science, Minhaj University Lahore, Lahore, 54000, Pakistan
6 School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
7 Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University Lahore Campus, Lahore, 54000, Pakistan

* Corresponding Author: Aaqib Inam. Email: email

(This article belongs to this Special Issue: Machine Learning and Computational Methods for Disease Detection and Prediction)

Intelligent Automation & Soft Computing 2021, 27(3), 701-712.


The outbreak of coronavirus disease 2019 (COVID-19) has had a tremendous effect on daily life and a great impact on the economy of the world. More than 200 countries have been affected. The diagnosis of coronavirus is a major challenge for medical experts. Early detection is one of the most effective ways to reduce the mortality rate and increase the chance of successful treatment. At this point in time, no antiviral drugs have been approved for use, and clinically approved vaccines have only recently become available in some countries. Hybrid artificial intelligence computer-aided systems for the diagnosis of disease are needed to help prevent the rapid spread of COVID-19. Various detection methods are being used to diagnose coronavirus. Deep extreme learning is the most successful artificial intelligence (AI) technique that efficiently supports medical experts in making smart decisions for the detection of COVID-19. In this study, a novel detection model to diagnose COVID-19 has been introduced to achieve a better accuracy rate. The study focuses on quantitative analysis and disease detection of COVID-19 empowered by a statistical real-time sequential deep extreme learning machine (D2C-RTS-DELM). The experimental results show 98.18% accuracy and 98.87% selectivity, and the probability of detection is 98.84%. The results demonstrate that the quantitative analysis and statistical real-time sequential deep extreme learning machine used in this study perform well in forecasting COVID-19 as well as in making timely decisions for treatment.


Cite This Article

A. Inam, Z. , A. Sarwar, S. , A. Atta et al., "Detection of covid-19 enhanced by a deep extreme learning machine," Intelligent Automation & Soft Computing, vol. 27, no.3, pp. 701–712, 2021.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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