Journal
INFORMATION SCIENCES
Volume 545, Issue -, Pages 403-414Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.09.041
Keywords
Neural Networks; Image Classification; COVID-19; GLCM; X-ray; Pneumonia
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In this study, supervised learning models were used to conduct experiments in accurately classifying medical images of COVID-19 patients and other related lung diseases. The goal was to lay the groundwork 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.
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. (C) 2020 Elsevier Inc. All rights reserved.
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