A Deep-Learning-Based Edge-Centric COVID-19-Like Pandemic Screening and Diagnosis System within a B5G Framework Using Blockchain
Abstract
Beyond 5G (B5G) has the potential of realizing all three pillars of 5G, which are massive type communication, ultra-reliable low latency communication, and enhanced mobile broadband. Currently, a COVID-19-like pandemic can utilize B5G to combat the pandemic by using real-time processing of massive volumes of test data at the edge of hospitals and by leveraging seamless communication between the edge and a global core cloud to update any diagnosis or predicting model globally. In this article, we propose an artificial-intelligence-enabled edge-centric COVID-19 screening and diagnosis system using the B5G network. Furthermore, we use blockchain-based secure transmission of patients' data in the edge. The proposed system uses screening and diagnosis in the edge by using powerful edge devices that can run deep learning (DL) models. The DL models can be downloaded from the core cloud to the edge server or uploaded to the core cloud when necessary.
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The prognosis of a patient infected with COVID-19 related illness is limited with high mortality.
“The prognosis of a patient infected with COVID-19 related illness is limited with high mortality [144, 145].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [145]
Cites
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Self-citationEdge Computing with Cloud for Voice Disorder Assessment and Treatment
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Self-citationFuture of Ultra-Dense Networks Beyond 5G: Harnessing Heterogeneous Moving Cells
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Self-citationComputing and Processing on the Edge: Smart Pathology Detection for Connected Healthcare
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Self-citationA Deep-Tree-Model-Based Radio Resource Distribution for 5G Networks
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Self-citationTree-Based Deep Networks for Edge Devices
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Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399 · see citing works
Self-citationA comprehensive survey on multimodal medical signals fusion for smart healthcare systems
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Self-citationCOVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images
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Self-citationBlockchain for Securing AI Applications and Open Innovations
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Pandemic Management for Diseases Similar to COVID-19 Using Deep Learning and 5G Communications
Muhammad, G., Alqahtani, S., & Alelaiwi, A. (2021) · IEEE Network · doi:10.1109/mnet.011.2000739 · Cited by 29 · see citing works
Self-citation
- Authors
- Muhammad, G., & Hossain, M. S.
- Affiliations
- King Saud University (SA)
- Venue
- IEEE Network
- Published
- 2021-03
- Volume
- 35
- Issue
- 2
- Pages
- 74-81
- ISSN
- 0890-8044 (ISSN-L)
- 1558-156X
- OpenAlex
- W3145876438
- Cited by
- 36
- References
- 14
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 5 · 2023: 6 · 2022: 13 · 2021: 8
- FWCI
- 3.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Network (publishedVersion)
- Funders
- King Saud University
- 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
- COVID-19 screening, edge computing, beyond 5G, blockchain, patient data security, deep learning models, remote diagnosis
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