Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Detection of Coronavirus Disease (COVID-19) Based on Deep Features

Version 1 : Received: 18 March 2020 / Approved: 19 March 2020 / Online: 19 March 2020 (13:49:49 CET)
Version 2 : Received: 19 April 2020 / Approved: 22 April 2020 / Online: 22 April 2020 (05:58:22 CEST)

How to cite: Sethy, P.K.; Behera, S.K. Detection of Coronavirus Disease (COVID-19) Based on Deep Features. Preprints 2020, 2020030300 (doi: 10.20944/preprints202003.0300.v1). Sethy, P.K.; Behera, S.K. Detection of Coronavirus Disease (COVID-19) Based on Deep Features. Preprints 2020, 2020030300 (doi: 10.20944/preprints202003.0300.v1).

Abstract

The detection of coronavirus (COVID-19) is now a critical task for the medical practitioner. The coronavirus spread so quickly between people and approaches 100,000 people worldwide. In this consequence, it is very much essential to identify the infected people so that prevention of spread can be taken. In this paper, the deep learning based methodology is suggested for detection of coronavirus infected patient using X-ray images. The support vector machine classifies the corona affected X-ray images from others using the deep feature. The methodology is beneficial for the medical practitioner for diagnosis of coronavirus infected patient. The suggested classification model, i.e. resnet50 plus SVM achieved accuracy, FPR, F1 score, MCC and Kappa are 95.38%,95.52%, 91.41% and 90.76% respectively for detecting COVID-19 (ignoring SARS, MERS and ARDS). The classification model ResNet50 plus SVM is superior compared to other classification models. The result is based on the data available in the repository of GitHub, Kaggle and Open-i as per their validated X-ray images.

Subject Areas

coronavirus; COVID-19; diagnosis; deep features; SVM

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