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Title: Residual learning diagnosis detection: an advanced residual learning diagnosis detection system for COVID-19 in industrial internet of things
Authors: Zhang, Mingdong
Chu, Ronghe
Dong, Chaoyu
Wei, Jianguo
Lu, Wenhuan
Xiong, Naixue
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Zhang, M., Chu, R., Dong, C., Wei, J., Lu, W. & Xiong, N. (2021). Residual learning diagnosis detection: an advanced residual learning diagnosis detection system for COVID-19 in industrial internet of things. IEEE Transactions On Industrial Informatics, 17(9), 6510-6518.
Journal: IEEE Transactions on Industrial Informatics
Abstract: Due to the fast transmission speed and severe health damage, COVID-19 has attracted global attention. Early diagnosis and isolation are effective and imperative strategies for epidemic prevention and control. Most diagnostic methods for the COVID-19 is based on nucleic acid testing (NAT), which is expensive and time-consuming. To build an efficient and valid alternative of NAT, this article investigates the feasibility of employing computed tomography images of lungs as the diagnostic signals. Unlike normal lungs, parts of the lungs infected with the COVID-19 developed lesions, ground-glass opacity, and bronchiectasis became apparent. Through a public dataset, in this article, we propose an advanced residual learning diagnosis detection (RLDD) scheme for the COVID-19 technique, which is designed to distinguish positive COVID-19 cases from heterogeneous lung images. Besides the advantage of high diagnosis effectiveness, the designed residual-based COVID-19 detection network can efficiently extract the lung features through small COVID-19 samples, which removes the pretraining requirement on other medical datasets. In the test set, we achieve an accuracy of 91.33%, a precision of 91.30%, and a recall of 90%. For the batch of 150 samples, the assessment time is only 4.7 s. Therefore, RLDD can be integrated into the application programming interface and embedded into the medical instrument to improve the detection efficiency of COVID-19.
ISSN: 1551-3203
DOI: 10.1109/TII.2021.3051952
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rights: © IEEE 2021. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:ERI@N Journal Articles

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