About: CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image

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About: CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image 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	CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image
Creator	Jahanban-Esfahlan, Rana Javaheri, Tahereh Seidi, Khaled Malekzadeh, Reza Amir, Reza »more»
source	ArXiv
abstract	Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists (70%). The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format. Open-source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership.
has issue date	2020-05-06(xsd:dateTime)
has license	arxiv
sha1sum (hex)	37f4f5809d518f25ef69970be4621d842979f2a7
resource representing a document's title	CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image
resource representing a document's body	covid:37f4f5809d518f25ef69970be4621d842979f2a7#body_text
is schema:about of	named entity 'radiologists' named entity 'sharing' named entity 'order' named entity 'lung diseases' covid:arg/37f4f5809d518f25ef69970be4621d842979f2a7 »more»

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