About: Automatic Detection of COVID-19 Using X-ray Images with Deep Convolutional Neural Networks and Machine Learning

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About: Automatic Detection of COVID-19 Using X-ray Images with Deep Convolutional Neural Networks and Machine Learning Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Automatic Detection of COVID-19 Using X-ray Images with Deep Convolutional Neural Networks and Machine Learning
Creator	Affiliations, * Asif, Sohaib Wenhui, Yi
source	MedRxiv
abstract	The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A vital step in the combat towards COVID-19 is a successful screening of contaminated patients, with one of the key screening approaches being radiological imaging using chest radiography. This study aimed to automatically detect COVID-19 pneumonia patients using digital chest x-ray images while maximizing the accuracy in detection using deep convolutional neural networks (DCNN). The dataset consists of 864 COVID-19, 1345 viral pneumonia and 1341 normal chest x-ray images. In this study, DCNN based model Inception V3 with transfer learning have been proposed for the detection of coronavirus pneumonia infected patients using chest X-ray radiographs and achieved more than 96% accuracy. The results demonstrate that transfer learning proved to be effective, showed robust performance and easily deployable approach for COVID-19 detection.
has issue date	2020-05-06(xsd:dateTime)
bibo:doi	10.1101/2020.05.01.20088211
has license	medrxiv
sha1sum (hex)	03499759c304ea827ecb34f6de18ad01bda688b7
schema:url	https://doi.org/10.1101/2020.05.01.20088211
resource representing a document's title	Automatic Detection of COVID-19 Using X-ray Images with Deep Convolutional Neural Networks and Machine Learning
resource representing a document's body	covid:03499759c304ea827ecb34f6de18ad01bda688b7#body_text
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