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All Journal Bulletin of Electrical Engineering and Informatics
Raid Rafi Omar Al-Nima
Northren Technical University
Electrical & Electronics Engineering

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Classifying healthy and infected Covid-19 cases by employing CT scan images Marwa Mawfaq Mohamedsheet Al-Hatab; Raid Rafi Omar Al-Nima; Maysaloon Abed Qasim
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4344

Abstract

A broad family of viruses called coronaviruses may infect people. The infection's symptoms are often relatively minor and resemble a normal cold. Since the coronavirus disease of 2019 (Covid-19) has never been observed in humans, anyone can contract it, and no one has an innate immunity to it. The detection of Covid-19 is now a critical task for medical practitioners. computed tomography (CT) scans can be considered as the best way to diagnose Covid-19. For patients with severe symptoms, imaging might help to assess the seriousness of the disease. Also, the CT scan can be helpful for determining a plan of care for a patient. This work focuses on classifying Covid-19 cases for healthy and infected by presenting a powerful scheme of recognizing CT scan images. In this study will be provided by proposing a model based on applying deep feature extractions with support vector machine (SVM). Big dataset of CT scan images is employed, it is available in the repository of GitHub and Kaggle. Remarkable result of 100% have been benchmarked as the highest evaluation after investigations. The proposed model can automatically detect between healthy and infected individuals.
Co-Authors Marwa Mawfaq Mohamedsheet Al-Hatab Maysaloon Abed Qasim