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A New Model Design for Combating COVID -19 Pandemic Based on SVM and CNN Approaches

Authors

  • Sura Monther Alnedawe Computer Engineering Department, College of Engineering, Al-Nahrain University, Baghdad, Iraq. https://orcid.org/0000-0003-1361-2607
  • Hadeel K. Aljobouri Biomedical Engineering Department, College of Engineering, Al-Nahrain University, Baghdad, Iraq. https://orcid.org/0000-0003-1792-9230

DOI:

https://doi.org/10.21123/bsj.2023.7403

Keywords:

CNN, COVID-19, Machine Learning, Medical Imaging, RNN, SVM

Abstract

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial lung CT-scans into two groups (COVID-19 and NonCOVID-19) had been proposed. A dataset used is 960 slices of CT scan collected from Iraqi patients /Ibn Al-Nafis teaching hospital. The performance metrics are used in this study (accuracy, recall, precision, and F1 scores). The results indicate that the proposed approach generated a high-quality model for the collected dataset, with an overall accuracy of 98.95% and an overall recall of 97 %.

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