Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 60750
Artificial Intelligence-Based Chest X-Ray Test of COVID-19 Patients

Authors: Dhurgham Al-Karawi, Nisreen Polus, Shakir Al-Zaidi, Sabah Jassim

Abstract:

The management of COVID-19 patients based on Chest Imaging is to turn out to be an essential tool for evaluating the spread of the pandemic, which is gripped the global community. It has already been used to monitor the situation of COVID-19 patients who has issues in respiratory status. There has been increased to use chest imaging for medical triage of patients who are showing moderate-severe clinical COVID-19 features; this is due to the fast dispersal of the pandemic to all continents and communities. This article demonstrates the development of machine learning techniques for the test of COVID-19 patients using Chest X-Ray (CXR) images in nearly real-time, to distinguish the COVID-19 infection with a significantly high level of accuracy. The testing performance has covered a combination of different datasets of CXRs images of positive COVID-19 patients, patients with viral and bacterial infections, also, people with a clear chest. The proposed AI scheme successfully distinguishes CXR scans of COVID-19 infected patients from CXR scans of viral and bacterial based pneumonia as well as normal cases with an average accuracy of 94.43%, sensitivity 95%, and specificity 93.86%. Predicted decisions would be supported by visual evidence to help clinicians speed up the initial assessment process of new suspected cases, especially in a resource-constrained environment.

Keywords: Artificial Intelligence, Texture Analysis, Gabor filter, COVID-19, chest x-ray scan, local binary pattern transform

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