About: Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia
Creator	Zhao, Hong Ruan, Lingxiang Li, Yongtao Wu, Wei Xu, Xiaowei Xu, Kaijin Du, Peng Jiang, ; Chen, ; Lang, Guanjing Li, Xukun Liang, ; Lv, Shuangzhi Ma, Chunlian Su, Junwei
topic	covid:1c56023b04a6fa6cad58de4332fb7ce830e658c3#this
Source	ArXiv
abstract	We found that the real time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a relatively low positive rate in the early stage to determine COVID-19 (named by the World Health Organization). The manifestations of computed tomography (CT) imaging of COVID-19 had their own characteristics, which are different from other types of viral pneumonia, such as Influenza-A viral pneumonia. Therefore, clinical doctors call for another early diagnostic criteria for this new type of pneumonia as soon as possible.This study aimed to establish an early screening model to distinguish COVID-19 pneumonia from Influenza-A viral pneumonia and healthy cases with pulmonary CT images using deep learning techniques. The candidate infection regions were first segmented out using a 3-dimensional deep learning model from pulmonary CT image set. These separated images were then categorized into COVID-19, Influenza-A viral pneumonia and irrelevant to infection groups, together with the corresponding confidence scores using a location-attention classification model. Finally the infection type and total confidence score of this CT case were calculated with Noisy-or Bayesian function.The experiments result of benchmark dataset showed that the overall accuracy was 86.7 % from the perspective of CT cases as a whole.The deep learning models established in this study were effective for the early screening of COVID-19 patients and demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.
has issue date	2020-02-21(xsd:dateTime)
has license	arxiv
sha1sum (hex)	1c56023b04a6fa6cad58de4332fb7ce830e658c3
resource representing a document's title	Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia
resource representing a document's body	covid:1c56023b04a6fa6cad58de4332fb7ce830e658c3#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/1c56023b04a6fa6cad58de4332fb7ce830e658c3
is schema:about of	named entity 'infection' named entity 'samples' named entity 'NASOPHARYNGEAL SWAB' named entity 'THESE' named entity 'DETECTION' named entity 'ATTENTION' named entity '224' named entity '175' named entity 'HEALTHY' named entity 'VIRAL PNEUMONIA' named entity 'IMAGES' named entity 'WORLD HEALTH ORGANIZATION' named entity 'COVID-19 PNEUMONIA' named entity 'LOW' named entity 'INFECTION' named entity 'GROUPS' named entity '110' named entity 'chain reaction' named entity 'groups' named entity 'function' named entity 'RT-PCR' named entity 'Bayesian' named entity 'type' named entity 'classification model' named entity 'study' named entity 'collected' named entity 'samples' named entity 'samples' named entity 'dimensional' named entity 'CT image' named entity 'pneumonia' named entity 'RT-PCR' named entity 'viral pneumonia' named entity 'diagnostic criteria' named entity 'viral pneumonia' named entity 'pneumonia' named entity 'infection' named entity 'Influenza' named entity 'viral pneumonia' named entity 'imaging' named entity 'separated' named entity 'RT-PCR' named entity 'viral pneumonia' named entity 'feature extraction' named entity 'pulmonary tuberculosis' named entity 'infection' named entity 'Influenza' named entity 'CNN' named entity 'China' named entity 'VNET' named entity 'pulmonary tuberculosis' named entity 'sputum' named entity 'convolutional neural network' named entity 'COVID' named entity 'COVID' named entity 'order of magnitude' named entity 'pneumonia' named entity 'infection' named entity 'infection' named entity 'fever' named entity 'digital image processing' named entity 'pleura' named entity 'location information' named entity 'myalgia' named entity 'pneumonia' named entity 'coronavirus disease 2019' named entity 'clinical experience' named entity 'diagnostic criteria' named entity 'infection' named entity 'confusion matrix' named entity 'COVID'

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