B5G and Explainable Deep Learning Assisted Healthcare Vertical at the Edge: COVID-I9 Perspective

• Published in: IEEE network Vol. 34; no. 4; pp. 98 - 105
• Main Authors: Rahman, Md. Abdur, Hossain, M. Shamim, Alrajeh, Nabil A., Guizani, Nadra
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Algorithms; Cloud computing; Coronaviruses; COVID-19; Datasets; Decision making; Deep learning; Edge computing; Health care; Hospitals; Image edge detection; Infectious diseases; Machine learning; Network latency; Semantics; Solid modeling; Training; Vaccines; Wireless networks
• DOI: 10.1109/MNET.011.2000353
• ISSN: 0890-8044

B5G-based tactile edge learning shows promise as a solution to handle infectious diseases such as COVID-19 at a global level. By leveraging edge computing with the 5G RAN, management of epidemic diseases such as COVID-19 can be conducted efficiently. Deploying a hierarchical edge computing architecture offers several benefits such as scalability, low latency, and privacy for the data and the training model, which enables analysis of COVID-19 by a local trusted edge server. However, existing deep learning (DL) algorithms suffer from two crucial drawbacks: first, the training requires a large COVID-19 dataset on various dimensions, which is difficult for any local authority to manage. Second, the DL results require ethical approval and explanations from healthcare providers and other stakeholders in order to be accepted. In this article, we propose a B5G framework that supports COVID-19 diagnosis, leveraging the low-latency, high-bandwidth features of the 5G network at the edge. Our framework employs a distributed DL paradigm where each COVID-19 edge employs its own local DL framework and uses a three-phase reconciliation with the global DL framework. The local DL model runs on edge nodes while the global DL model runs on a cloud environment. The training of a local DL model is performed with the dataset available from the edge; it is applied to the global model after receiving approval from the subject matter experts at the edge. Our framework adds semantics to existing DL models so that human domain experts on COVID-19 can gain insight and semantic visualization of the key decision-making activities that take place within the deep learning ecosystem. We have implemented a subset of various COVID-19 scenarios using distributed DL at the edge and in the cloud. The test results are promising.