Trustworthy and Intelligent COVID-19 Diagnostic IoMT through XR and Deep-Learning-Based Clinic Data Access


This article presents a novel extended reality (XR) and deep-learning-based Internet-of-Medical-Things (IoMT) solution for the COVID-19 telemedicine diagnostic, which systematically combines virtual reality/augmented reality (AR) remote surgical plan/rehearse hardware, customized 5G cloud computing and deep learning algorithms to provide real-time COVID-19 treatment scheme clues. Compared to existing perception therapy techniques, our new technique can significantly improve performance and security. The system collected 25 clinic data from the 347 positive and 2270 negative COVID-19 patients in the Red Zone by 5G transmission. After that, a novel auxiliary classifier generative adversarial network-based intelligent prediction algorithm is conducted to train the new COVID-19 prediction model. Furthermore, The Copycat network is employed for the model stealing and attack for the IoMT to improve the security performance. To simplify the user interface and achieve an excellent user experience, we combined the Red Zone's guiding images with the Green Zone's view through the AR navigate clue by using 5G. The XR surgical plan/rehearse framework is designed, including all COVID-19 surgical requisite details that were developed with a real-time response guaranteed. The accuracy, recall, F1-score, and area under the ROC curve (AUC) area of our new IoMT were 0.92, 0.98, 0.95, and 0.98, respectively, which outperforms the existing perception techniques with significantly higher accuracy performance. The model stealing also has excellent performance, with the AUC area of 0.90 in Copycat slightly lower than the original model. This study suggests a new framework in the COVID-19 diagnostic integration and opens the new research about the integration of XR and deep learning for IoMT implementation.

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Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Funding Information: This work was supported in part by the Yunnan Key Laboratory of Opto-Electronic Information Technology of Yunnan Normal University and in part by the National Natural Science Foundation of China under Grant 62062069, Grant 62062070, and Grant 62005235. The work of Victor Chang was supported in part by VC Research (VCR) under Grant 0000113.
Uncontrolled Keywords: Auxiliary classifier generative adversarial network (ACGAN),COVID-19,extended reality (XR),Internet of Medical Things (IoMT),security,Signal Processing,Information Systems,Hardware and Architecture,Computer Science Applications,Computer Networks and Communications
Publication ISSN: 2327-4662
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-11-01
Published Online Date: 2021-02-01
Accepted Date: 2021-02-01
Authors: Tai, Yonghang
Gao, Bixuan
Li, Qiong
Yu, Zhengtao
Zhu, Chunsheng
Chang, Victor (ORCID Profile 0000-0002-8012-5852)



Version: Accepted Version

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