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

Tai，Yonghang1; Gao，Bixuan1; Li，Qiong1; Yu，Zhengtao2; Zhu，Chunsheng3; Chang，Victor4

2021-11-01

10.1109/JIOT.2021.3055804

IEEE Internet of Things Journal   影响因子和分区

2327-4662

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.

Auxiliary classifier generative adversarial network (ACGAN) COVID-19 extended reality (XR) Internet of Medical Things (IoMT) security

SCI ; EI

英语

其他

National Natural Science Foundation of China[62005235];National Natural Science Foundation of China[62062069];National Natural Science Foundation of China[62062070];

WOS:000711808500033

20210709925794

5G mobile communication systems ; Diagnosis ; Learning algorithms ; Predictive analytics ; Surgery ; User experience ; User interfaces

Medicine and Pharmacology:461.6 ; Computer Peripheral Equipment:722.2

2-s2.0-85100776657

Scopus

1.Yunnan Key Laboratory of Opto-Electronic Information Technology,Yunnan Normal University,Kunming,650500,China
2.Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming,650093,China
3.Southern University of Science and Technology,Shenzhen,518055,China
4.Teesside University,Middlesbrough,TS1 3BA,United Kingdom

Tai，Yonghang,Gao，Bixuan,Li，Qiong,et al. Trustworthy and Intelligent COVID-19 Diagnostic IoMT through XR and Deep-Learning-Based Clinic Data Access[J]. IEEE Internet of Things Journal,2021,8(21):15965-15976.

Tai，Yonghang,Gao，Bixuan,Li，Qiong,Yu，Zhengtao,Zhu，Chunsheng,&Chang，Victor.(2021).Trustworthy and Intelligent COVID-19 Diagnostic IoMT through XR and Deep-Learning-Based Clinic Data Access.IEEE Internet of Things Journal,8(21),15965-15976.

Tai，Yonghang,et al."Trustworthy and Intelligent COVID-19 Diagnostic IoMT through XR and Deep-Learning-Based Clinic Data Access".IEEE Internet of Things Journal 8.21(2021):15965-15976.