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10.1371/journal.pone.0235187

http://scihub22266oqcxt.onion/10.1371/journal.pone.0235187
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32589673!7319603!32589673
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suck abstract from ncbi


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pmid32589673      PLoS+One 2020 ; 15 (6): e0235187
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  • New machine learning method for image-based diagnosis of COVID-19 #MMPMID32589673
  • Elaziz MA; Hosny KM; Salah A; Darwish MM; Lu S; Sahlol AT
  • PLoS One 2020[]; 15 (6): e0235187 PMID32589673show ga
  • COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.
  • |*Machine Learning[MESH]
  • |Adult[MESH]
  • |Aged[MESH]
  • |Aged, 80 and over[MESH]
  • |Algorithms[MESH]
  • |Betacoronavirus[MESH]
  • |COVID-19[MESH]
  • |Coronavirus Infections/*diagnostic imaging[MESH]
  • |Female[MESH]
  • |Humans[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Pandemics[MESH]
  • |Pneumonia, Viral/*diagnostic imaging[MESH]
  • |Radiography, Thoracic[MESH]
  • |SARS-CoV-2[MESH]
  • |Thorax/diagnostic imaging[MESH]


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