Convolutional support vector models: prediction of coronavirus disease using chest X-rays

Maia, Mateus, Pimentel, Jonatha S., Pereira, Ivalbert S., Gondim, João, Barreto, Marcos E. ORCID: 0000-0002-7818-1855 and Ara, Anderson (2020) Convolutional support vector models: prediction of coronavirus disease using chest X-rays. Information, 11 (12). ISSN 2078-2489

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Abstract

The disease caused by the new coronavirus (COVID-19) has been plaguing the world for months and the number of cases are growing more rapidly as the days go by. Therefore, finding a way to identify who has the causative virus is impressive, in order to find a way to stop its proliferation. In this paper, a complete and applied study of convolutional support machines will be presented to classify patients infected with COVID-19 using X-ray data and comparing them with traditional convolutional neural network (CNN). Based on the fitted models, it was possible to observe that the convolutional support vector machine with the polynomial kernel (CSVMPol) has a better predictive performance. In addition to the results obtained based on real images, the behavior of the models studied was observed through simulated images, where it was possible to observe the advantages of support vector machine (SVM) models.

Item Type:	Article
Official URL:	https://www.mdpi.com/journal/information
Additional Information:	© 2020 The Authors
Divisions:	Statistics
Subjects:	R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine H Social Sciences > HV Social pathology. Social and public welfare. Criminology H Social Sciences > HA Statistics
Date Deposited:	03 Aug 2022 07:45
Last Modified:	17 Oct 2024 16:15
URI:	http://eprints.lse.ac.uk/id/eprint/115769

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