Deep Learning-based Classification of COVID-19 Lung Ultrasound for Tele-operative Robot-assisted diagnosis
1st International Conference on Multidisciplinary Engineering and Applied Science, ICMEAS 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1774658
ABSTRACT
Despite the implementation of strict COVID-19 guideline, over 300,000 healthcare workers has been infected with COVID-19 globally with over 7,000 deaths. This risk of infection and loss of vital healthcare workers can be eliminated by deploying a deep learning enhanced teleoperated robot. The robot for this study was developed by Worchester Polytechnic Institute, US, to be deployed for COVID-19 at the Nigerian National Hospital Abuja. In this paper, we develop a deep learning-based automatic classification of lung ultrasound images for rapid, efficient and accurate diagnosis of patients for the developed teleoperated robot. Two lightweight models (SqueezeNet and MobileNetV2) were trained on COVID-US benchmark dataset with a computational-and memory-efficient mixed-precision training. The models achieve 99.74% (± 1) accuracy, 99.39% (± 1) recall and 99.58% (± 2) precision rate. We believe that a timely deployment of this model on the teleoperated robot will remove the risk of infection of healthcare workers. © 2021 IEEE.
Deep convolution neural networks; healthcare workers; Lung Ultrasound; mixed-precision training; Robot-assisted diagnosis; Tele-medicine; Biological organs; Classification (of information); Computer aided diagnosis; Deep neural networks; Health care; Occupational risks; Robots; Convolution neural network; Deep convolution neural network; Mixed precision; Nigerians; Robot-assisted diagnose; Teleoperated robots; Ultrasonics
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
1st International Conference on Multidisciplinary Engineering and Applied Science, ICMEAS 2021
Year:
2021
Document Type:
Article
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