AI-Enabled Health 4.0: An IoT-Based COVID-19 Diagnosis Use-Case
Source of Publication
GLOBECOM 2022 - 2022 IEEE Global Communications Conference
The Internet of Things (IoT) has revamped service-oriented architectures by enabling edge-based devices to collect and share information that is vital for the service provisioning process. IoT devices have evolved from simple data acquirers and have become part of the service provisioning process. These devices are now able to sense, acquire, communicate, and process data in an intelligent manner. With the support of Artificial Intelligence (AI), IoT devices can now support users with minimal reliance on centralized entities, such as the Cloud. IoT devices are now able to share raw and processed information securely, without or with minimal reliance on centralized devices. This paper proposes a general framework for Health 4.0 to provide edge-based health services with the support of AI. IoT devices collect and share patient information in a secure manner to enable user-side disease diagnosis. The solution enables both federated and centralized learning to coexist under one framework. As a proof-of-concept, the solution considers a COVID-19 diagnosis use-case. A Machine Learning (ML) web-based user application is developed to analyze frontal chest X-ray (CXR) images and make predictions on whether patients' lungs are damaged. The solution provides an experimental study on mechanisms and approaches needed to increase learning accuracy with reduced dataset sizes and image quality through Federated Learning (FL).
COVID-19, Performance evaluation, Image edge detection, Service-oriented architecture, Internet of Things, Medical diagnosis, vServers
Song, Xiaoyu; Pan, Wei; Ridhawi, Ismaeel Al; Abbas, Ali; and Otoum, Safa, "AI-Enabled Health 4.0: An IoT-Based COVID-19 Diagnosis Use-Case" (2022). All Works. 5578.
Indexed in Scopus