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

This version is not peer-reviewed.

Submitted:

18 March 2020

Posted:

19 March 2020

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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.
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

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