A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks

Sergio Varela-Santos, Patricia Melin

Research output: Contribution to journalArticle

Abstract

© 2020 Elsevier Inc. Since the recent challenge that humanity is facing against COVID-19, several initiatives have been put forward with the goal of creating measures to help control the spread of the pandemic. In this paper we present a series of experiments using supervised learning models in order to perform an accurate classification on datasets consisting of medical images from COVID-19 patients and medical images of several other related diseases affecting the lungs. This work represents an initial experimentation using image texture feature descriptors, feed-forward and convolutional neural networks on newly created databases with COVID-19 images. The goal was setting a baseline for the future development of a system capable of automatically detecting the COVID-19 disease based on its manifestation on chest X-rays and computerized tomography images of the lungs.
Original languageEnglish
JournalInformation Sciences
Volume545
DOIs
StatePublished - 4 Feb 2021

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