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Specialized COVID-19 detection techniques with machine learning
J. Phys. Conf. Ser. ; 1797, 2021.
Article in English | Scopus | ID: covidwho-1139927
ABSTRACT
COVID-19 has been declared as a pandemic in over 200 countries of the world.COVID-19 is an infectious disease that is primarily caused by severe acute respiratory syndrome coronavirus 2((SARS-CoV-2). According to the latest figures by the world health organization, the number of confirmed cases for the COVID-19 pandemic worldwide is more than 20 million worldwide and the number of fatalities reported is over 700,000. It has been found from several studies that medical imaging coupled with machine learning methods holds great promise in the detection and follow-up of the COVID-19 disease due to the enhanced accuracy in results of the experiments performed by the researchers. Machine Learning (ML)-based solutions can be used to simultaneously analyze multiple input computed tomography (CT) images of chest and lungs. A large number of papers have been published that show the application of machine learning methods in successful detection of the COVID-19 disease. Such applications demonstrate the suitability of feature prediction, identification of involved risks and therefore managing and intercepting the outbreak of such diseases. This paper describes some of the techniques in machine learning that can be used detection of COVID-19 disease. © 2021 Institute of Physics Publishing. All rights reserved.

Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: J. Phys. Conf. Ser. Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: J. Phys. Conf. Ser. Year: 2021 Document Type: Article