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10.1007/s10096-020-03901-z

http://scihub22266oqcxt.onion/10.1007/s10096-020-03901-z
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32337662!7183816!32337662
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suck abstract from ncbi


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pmid32337662      Eur+J+Clin+Microbiol+Infect+Dis 2020 ; 39 (7): 1379-1389
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  • Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks #MMPMID32337662
  • Singh D; Kumar V; Vaishali; Kaur M
  • Eur J Clin Microbiol Infect Dis 2020[Jul]; 39 (7): 1379-1389 PMID32337662show ga
  • Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.
  • |*Deep Learning[MESH]
  • |Betacoronavirus[MESH]
  • |COVID-19[MESH]
  • |COVID-19 Testing[MESH]
  • |Clinical Laboratory Techniques[MESH]
  • |Coronavirus Infections/diagnosis/*diagnostic imaging[MESH]
  • |False Negative Reactions[MESH]
  • |False Positive Reactions[MESH]
  • |Humans[MESH]
  • |Image Interpretation, Computer-Assisted/*methods[MESH]
  • |Lung/*diagnostic imaging[MESH]
  • |Neural Networks, Computer[MESH]
  • |Pandemics[MESH]
  • |Pneumonia, Viral/diagnosis/*diagnostic imaging[MESH]
  • |Reverse Transcriptase Polymerase Chain Reaction[MESH]
  • |SARS-CoV-2[MESH]
  • |Sensitivity and Specificity[MESH]


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