Open Access

ARTICLE

Screening of COVID-19 Patients Using Deep Learning and IoT Framework

Harshit Kaushik¹, Dilbag Singh², Shailendra Tiwari³, Manjit Kaur², Chang-Won Jeong⁴, Yunyoung Nam^5,*, Muhammad Attique Khan⁶

1 Manipal University Jaipur, India
2 Bennett University, Greater Noida, India
3 Thapar Institute of Engineering and Technology, Patiala, India
4 Medical Convergence Research Center, Wonkwang University, Iksan, Korea
5 Department of Computer Science and Engineering, Soonchunhyang University, Asan, Korea
6 Department of Computer Science, HITEC University, Taxila, Pakistan

* Corresponding Author: Yunyoung Nam. Email:

(This article belongs to the Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)

Computers, Materials & Continua 2021, 69(3), 3459-3475. https://doi.org/10.32604/cmc.2021.017337

Received 27 January 2021; Accepted 14 April 2021; Issue published 24 August 2021

Abstract

In March 2020, the World Health Organization declared the coronavirus disease (COVID-19) outbreak as a pandemic due to its uncontrolled global spread. Reverse transcription polymerase chain reaction is a laboratory test that is widely used for the diagnosis of this deadly disease. However, the limited availability of testing kits and qualified staff and the drastically increasing number of cases have hampered massive testing. To handle COVID-19 testing problems, we apply the Internet of Things and artificial intelligence to achieve self-adaptive, secure, and fast resource allocation, real-time tracking, remote screening, and patient monitoring. In addition, we implement a cloud platform for efficient spectrum utilization. Thus, we propose a cloud-based intelligent system for remote COVID-19 screening using cognitive-radio-based Internet of Things and deep learning. Specifically, a deep learning technique recognizes radiographic patterns in chest computed tomography (CT) scans. To this end, contrast-limited adaptive histogram equalization is applied to an input CT scan followed by bilateral filtering to enhance the spatial quality. The image quality assessment of the CT scan is performed using the blind/referenceless image spatial quality evaluator. Then, a deep transfer learning model, VGG-16, is trained to diagnose a suspected CT scan as either COVID-19 positive or negative. Experimental results demonstrate that the proposed VGG-16 model outperforms existing COVID-19 screening models regarding accuracy, sensitivity, and specificity. The results obtained from the proposed system can be verified by doctors and sent to remote places through the Internet.

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