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

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has 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
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