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 a...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2023 |
| País: | España |
| Recursos: | Universitat Autònoma de Barcelona |
| Repositório: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglês |
| OAI Identifier: | oai:ddd.uab.cat:283778 |
| Acesso em linha: | https://ddd.uab.cat/record/283778 https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1601 |
| Access Level: | Acceso aberto |
| Palavra-chave: | COVID-19 Support Vector Machine (SVM) VGG19 AlexNet ResNet50 |
| Resumo: | 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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