About: Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning
Creator	Zhang, Wei Zhu, Xue Cong, Jinyu Han, Zhongyi Hong, Yanfei »more»
source	ArXiv
abstract	This paper addresses the new problem of automated screening of coronavirus disease 2019 (COVID-19) based on chest X-rays, which is urgently demanded toward fast stopping the pandemic. However, robust and accurate screening of COVID-19 from chest X-rays is still a globally recognized challenge because of two bottlenecks: 1) imaging features of COVID-19 share some similarities with other pneumonia on chest X-rays, and 2) the misdiagnosis rate of COVID-19 is very high, and the misdiagnosis cost is expensive. While a few pioneering works have made much progress, they underestimate both crucial bottlenecks. In this paper, we report our solution, discriminative cost-sensitive learning (DCSL), which should be the choice if the clinical needs the assisted screening of COVID-19 from chest X-rays. DCSL combines both advantages from fine-grained classification and cost-sensitive learning. Firstly, DCSL develops a conditional center loss that learns deep discriminative representation. Secondly, DCSL establishes score-level cost-sensitive learning that can adaptively enlarge the cost of misclassifying COVID-19 examples into other classes. DCSL is so flexible that it can apply in any deep neural network. We collected a large-scale multi-class dataset comprised of 2,239 chest X-ray examples: 239 examples from confirmed COVID-19 cases, 1,000 examples with confirmed bacterial or viral pneumonia cases, and 1,000 examples of healthy people. Extensive experiments on the three-class classification show that our algorithm remarkably outperforms state-of-the-art algorithms. It achieves an accuracy of 97.01%, a precision of 97%, a sensitivity of 97.09%, and an F1-score of 96.98%. These results endow our algorithm as an efficient tool for the fast large-scale screening of COVID-19.
has issue date	2020-04-27(xsd:dateTime)
has license	arxiv
sha1sum (hex)	bd3ebd845f11be54ca6d180b6834ffa528f8ea5f
resource representing a document's title	Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning
resource representing a document's body	covid:bd3ebd845f11be54ca6d180b6834ffa528f8ea5f#body_text
is schema:about of	named entity 'accurate' named entity 'COVID-19' named entity 'cost' named entity 'expensive' named entity 'COVID-19' »more»

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