LAUSR

COVID-19 is a vital zoonotic illness caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) COVID-19 is a very wide-spread among humans thus the early detection and curing of the disease offers a high opportunity of survival for patients Computed Tomography (CT) plays an important role in the diagnosis of COVID-19 As chest radiography can give an indicator of coronavirus Though, an automated Computer Aided Diagnostic (CAD) system for COVID-19 based on chest X-Ray image analysis is presented in this article It is designed for COVID-19 recognition from other MERS, SARS, and ARDS viral pneumonia The optimal threshold value for the segmentation of a chest image is deduced by exploiting Li s' method and particle swarm intelligence Laws' masks are then applied to the segmented chest image for secondary characteristics highlighting After that, nine different vectors of attributes are extracted from the Grey Level Co-occurrence Matrix (GLCM) representation of each Law's mask result Support vector machine ensemble models are then built based on the extracted feature vectors Finally, a weighted voting method is utilized to combine the decisions of ensemble classifiers Experimental findings show an accuracy of 98 04 % It indicates that the suggested CAD scheme can be a promising supplementary COVID-19 diagnostic tool for clinical doctors © 2020, Intelligent Network and Systems Society