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

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.

Automated summary

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.