Cited by 347IoT and Edge/Fog ComputingCOVID-19 diagnosis using AIInternet of Things and AI

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Explainable AI and Mass Surveillance System-Based Healthcare Framework to Combat COVID-I9 Like Pandemics

Hossain, M. Shamim, Muhammad, Ghulam, Guizani, Nadra·2020·IEEE Network

DOI 10.1109/mnet.011.2000458

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Abstract

Tactile edge technology that focuses on 5G or beyond 5G reveals an exciting approach to control infectious diseases such as COVID-19 internationally. The control of epidemics such as COVID-19 can be managed effectively by exploiting edge computation through the 5G wireless connectivity network. The implementation of a hierarchical edge computing system provides many advantages, such as low latency, scalability, and the protection of application and training model data, enabling COVID-19 to be evaluated by a dependable local edge server. In addition, many deep learning (DL) algorithms suffer from two crucial disadvantages: first, training requires a large COVID-19 dataset consisting of various aspects, which will pose challenges for local councils; second, to acknowledge the outcome, the findings of deep learning require ethical acceptance and clarification by the health care sector, as well as other contributors. In this article, we propose a B5G framework that utilizes the 5G network's low-latency, high-bandwidth functionality to detect COVID-19 using chest X-ray or CT scan images, and to develop a mass surveillance system to monitor social distancing, mask wearing, and body temperature. Three DL models, ResNet50, Deep tree, and Inception v3, are investigated in the proposed framework. Furthermore, blockchain technology is also used to ensure the security of healthcare data.

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Authors: Hossain, M. S., Muhammad, G., & Guizani, N.
Affiliations: King Saud University (SA)
Washington State University (US)
ORCID: Hossain, M. Shamim
Muhammad, Ghulam
Guizani, Nadra
Venue: IEEE Network
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-07
Volume: 34
Issue: 4
Pages: 126-132
ISSN: 0890-8044 (ISSN-L)
1558-156X
DOI: 10.1109/mnet.011.2000458
OpenAlex: W3041006163
Cited by: 347
References: 16
Citations by year: 2026: 14 · 2025: 35 · 2024: 36 · 2023: 58 · 2022: 69 · 2021: 112 · 2020: 23
FWCI: 30.78
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-009 (stm-asf)
doi.org/10.15223/policy-001 (stm-asf)
Locations: IEEE Network (publishedVersion)
Funders: King Saud University (RGP 228)
Topics: Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Keywords: explainable AI, COVID-19 detection, mass surveillance, edge computing, 5G, beyond 5G, chest radiography, computed tomography, social distancing monitoring, face mask detection, body temperature monitoring, blockchain, healthcare data security, deep learning models, ResNet50, InceptionV3

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Explainable AI and Mass Surveillance System-Based Healthcare Framework to Combat COVID-I9 Like Pandemics

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