Enhanced SVM based Covid 19 detection system using efficient transfer learning algorithms

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(University Kasdi Merbah Ouargla)
Bensid, Khaled

(University Kasdi Merbah Ouargla)
Lati, Ibtissem (University Kasdi Merbah Ouargla)
Gezzal, Chahra (University Kasdi Merbah Ouargla)

Fecha:	2023
Resumen:	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.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	COVID-19 ; Support Vector Machine (SVM) ; VGG19 ; AlexNet ; ResNet50
Publicado en:	ELCVIA. Electronic letters on computer vision and image analysis, Vol. 22 Núm. 1 (2023) , p 77-88 (Regular Issue) , ISSN 1577-5097

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