Enhanced SVM Based Covid 19 Detection System Using Efficient Transfer Learning Algorithms


  • Abdelhai LATI Faculty of New information and communication technologies University Kasdi Merbah Ouargla (UKMO), BP 511, 30000, Ouargla, Algeria
  • Khaled BENSID Lab. de Génie Electrique (LAGE)
  • Ibtissem LATI Faculty of Medicine,Ouargla (UKMO), BP 511, 30000, Ouargla. Algeria.
  • Chahra GEZZAL Lab. de Génie Electrique (LAGE),Faculty of New information and communication technologies University Kasdi Merbah Ouargla (UKMO), BP 511, 30000, Ouargla. Algeria


The detection of the novel coronavirus disease (COVID-19) has recently become a critical task for medical diagnosis. Knowing that deep Learning is an advanced area of machine learning that has gained much of interest, especially convolutional neural network. It has been widely used in a variety of applications. Since it has been proved that transfer learning is effective for the medical classification tasks,
in this study; COVID -19 detection system is implemented as a quick alternative, accurate and reliable diagnosis option to detect COVID-19 disease. Three pre-trained convolutional neural network based models (ResNet50, VGG19, AlexNet) have been proposed for this system. Based on the obtained performance results, the pre-trained models with support vector machine (SVM) provide the best classification performance compared to the used models individually.


COVID-19, Support Vector Machine (SVM), VGG19, AlexNet, ResNet50


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