ABSTRACT
SARS-CoV-2 is a strain of coronavirus in the Orthocoronavirinae subfamily, which was first identified in Wuhan, China, in December 2019. COVID-19 is the name of the disease produced by SARS-CoV-2. Because SARS-CoV-2 is cytopathic to airway epithelial cells and alveolar cells, researchers showed that combining features extracted from radiographic images with laboratory results may significantly aid in the early detection of the infection. Standard features seen in radiographs of patients with coronavirus are infiltrated or patchy opacities, like several features of other forms of viral pneumonia. In the early stages of COVID-19 infection, no abnormalities can be observed on radiographic images. However, as the disease unfolds, COVID-19 progressively manifests as a typical unilateral inflammation involving the middle and upper or lower lungs, sometimes with consolidation. Almost every hospital has at least one radiographer routinely used for diagnosing pneumonia, lymph nodes, and other conditions. Unfortunately, acquiring radiographic images and analyzing COVID-19 analyzing by a radiologist is time-consuming. Therefore, this work aims to assess the detection capability of the viral respiratory disease caused by the SARSCoV-2 virus from radiographic images using a deep transfer learning model implemented in a development environment for numerical computation and statistical analysis called MATLAB. © 2022 IEEE.