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), 2022)
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.