A survey into COVID-19 Induced Pneumonia Detection and Feasibility of using UWB Medical Imaging

Abstract

This paper presents a survey into the currently
thriving research on using machine learning for COVID-19
induced pneumonia detection through the use of radiographic
scans, presents a brief review of the methodologies and assesses
the classification results, and finally presents an alternative in the
form of ultrawideband (UWB) imaging. Few works on UWB
imaging is investigated and used as a source of inspiration for
developing an UWB imaging system for detection of
accumulation of fluid in lungs. The goal is to extract information
about dielectric property variation from backscattered UWB
signals to detect pneumonia caused by COVID-19. An edge fed
Vivaldi antenna along with a multilayer planar model for lung is
simulated in CST microwave studio and subjected to UWB
excitation. The backscattered signals in the form of S-parameters
are analyzed with various Delay-and-Sum (DAS) algorithms and
images are constructed for lung tissues of different permittivity
and conductivity, where higher values are supported to allude to
the infected lungs.