Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Shaheen Hammadi, Saad
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Coronavirus disease 2019 detection using deep features learning A. Khalaf, Zainab; Shaheen Hammadi, Saad; Khattar Mousa, Alaa; Murtada Ali, Hanan; Ramadhan Alnajar, Hanan; Hashim Mohsin, Raghdan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4364-4372

Abstract

A Coronavirus disease 2019 (COVID-19) pandemic detection considers a critical and challenging task for the medical practitioner. The coronavirus disease spread so rapidly between people and infected more than one hundred and seventy million people worldwide. For this reason, it is necessary to detect infected people with coronavirus and take action to prevent virus spread. In this study, a COVID-19 classification methodology was adopted to detect infected people using computed tomography (CT) images. Deep learning was applied to recognize COVID-19 infected cases for different patients by employing deep features. This methodology can be beneficial for medical practitioners to diagnose infected patients. The results were based on a new data collection named BasrahDataset that includes different CT scan videos for Iraqi patients. The proposed system gave promised results with a 99% F1-score for detecting COVID-19.
Co-Authors A. Khalaf, Zainab Hashim Mohsin, Raghdan Khattar Mousa, Alaa Murtada Ali, Hanan Ramadhan Alnajar, Hanan