Deep Learning-based Classification of COVID-19 Lung Ultrasound for Tele-operative Robot-assisted diagnosis

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 o f l ung u ultrasound images f or rapid, efficient a nd a ccurate d iagnosis o f p atients f or t he 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.