Enhanced SVM Based Covid 19 Detection System Using Efficient Transfer Learning Algorithms
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Computer Vision Center Press
2023-10-01
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| Series: | ELCVIA Electronic Letters on Computer Vision and Image Analysis |
| Subjects: | |
| Online Access: | https://elcvia.cvc.uab.cat/article/view/1601 |
| _version_ | 1797650852523016192 |
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| author | Abdelhai LATI Khaled BENSID Ibtissem LATI Chahra GEZZAL |
| author_facet | Abdelhai LATI Khaled BENSID Ibtissem LATI Chahra GEZZAL |
| author_sort | Abdelhai LATI |
| collection | DOAJ |
| description |
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.
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| first_indexed | 2024-03-11T16:06:39Z |
| format | Article |
| id | doaj.art-cc24a55f127e48c6807ba9495d9d702e |
| institution | Directory Open Access Journal |
| issn | 1577-5097 |
| language | English |
| last_indexed | 2024-03-11T16:06:39Z |
| publishDate | 2023-10-01 |
| publisher | Computer Vision Center Press |
| record_format | Article |
| series | ELCVIA Electronic Letters on Computer Vision and Image Analysis |
| spelling | doaj.art-cc24a55f127e48c6807ba9495d9d702e2023-10-25T00:59:20ZengComputer Vision Center PressELCVIA Electronic Letters on Computer Vision and Image Analysis1577-50972023-10-0122110.5565/rev/elcvia.1601Enhanced SVM Based Covid 19 Detection System Using Efficient Transfer Learning Algorithms Abdelhai LATI0Khaled BENSID1Ibtissem LATI2Chahra GEZZAL3Faculty of New information and communication technologies University Kasdi Merbah Ouargla (UKMO), BP 511, 30000, Ouargla, AlgeriaLab. de Génie Electrique (LAGE)Faculty of Medicine,Ouargla (UKMO), BP 511, 30000, Ouargla. Algeria.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. https://elcvia.cvc.uab.cat/article/view/1601COVID-19Support Vector Machine (SVM)VGG19AlexNetResNet50 |
| spellingShingle | Abdelhai LATI Khaled BENSID Ibtissem LATI Chahra GEZZAL Enhanced SVM Based Covid 19 Detection System Using Efficient Transfer Learning Algorithms ELCVIA Electronic Letters on Computer Vision and Image Analysis COVID-19 Support Vector Machine (SVM) VGG19 AlexNet ResNet50 |
| title | Enhanced SVM Based Covid 19 Detection System Using Efficient Transfer Learning Algorithms |
| title_full | Enhanced SVM Based Covid 19 Detection System Using Efficient Transfer Learning Algorithms |
| title_fullStr | Enhanced SVM Based Covid 19 Detection System Using Efficient Transfer Learning Algorithms |
| title_full_unstemmed | Enhanced SVM Based Covid 19 Detection System Using Efficient Transfer Learning Algorithms |
| title_short | Enhanced SVM Based Covid 19 Detection System Using Efficient Transfer Learning Algorithms |
| title_sort | enhanced svm based covid 19 detection system using efficient transfer learning algorithms |
| topic | COVID-19 Support Vector Machine (SVM) VGG19 AlexNet ResNet50 |
| url | https://elcvia.cvc.uab.cat/article/view/1601 |
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