About: Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer Learning

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer 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	Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer Learning
Creator	Khan, Ahmed Naz, Saeeda Razzak, Imran Rehman, Arshia Zaib, Ahmad
source	MedRxiv
abstract	Background: Coronavirus disease (COVID-19) is an infectious disease caused by a new virus. Exponential growth is not only threatening lives, but also impacting businesses and disrupting travel around the world. Aim: The aim of this work is to develop an efficient diagnosis of COVID-19 disease by differentiating it from viral pneumonia, bacterial pneumonia and healthy cases using deep learning techniques. Method: In this work, we have used pre-trained knowledge to improve the diagnostic performance using transfer learning techniques and compared the performance different CNN architectures. Results: Evaluation results using K-fold (10) showed that we have achieved state of the art performance with overall accuracy of 98.75% on the perspective of CT and X-ray cases as a whole. Conclusion: Quantitative evaluation showed high accuracy for automatic diagnosis of COVID-19. Pre-trained deep learning models develop in this study could be used early screening of coronavirus, however it calls for extensive need to CT or X-rays dataset to develop a reliable application.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1101/2020.04.11.20054643
has license	medrxiv
sha1sum (hex)	c87263c070e45a7e0957814073f94d3dee040113
schema:url	https://doi.org/10.1101/2020.04.11.20054643
resource representing a document's title	Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer Learning
resource representing a document's body	covid:c87263c070e45a7e0957814073f94d3dee040113#body_text
is schema:about of	named entity 'compared' named entity 'disease' named entity 'Results' named entity 'COULD BE' named entity 'CONCLUSION' »more»

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software