A Hybrid Model for Covid-19 Detection using CT-Scans

Abstract: Although some believe it has been wiped out, the coronavirus is striking again. Controlling this epidemic necessitates early detection of coronavirus disease. Computed tomography (CT) scan images allow fast and accurate screening for COVID-19. This study seeks to develop the most precise model for identifying and classifying COVID-19 by developing an automated approach using transfer-learning CNN models as a base. Transfer learning models like VGG16, Resnet50, and Xception are employed in this study. The VGG16 has a 98.39% accuracy, the Resnet50 has a 97.27% accuracy, and the Xception has a 96.6% accuracy; after that, a hybrid model made using the stacking ensemble method has an accuracy of 98.71%. According to the findings, hybrid architecture offers greater accuracy than a single architecture.

Nagwa G. Ali, Fahad K. El Sheref and Mahmoud M. El khouly. “A Hybrid Model for Covid-19 Detection using CT-Scans”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.3 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140372

@article{Ali2023,
title = {A Hybrid Model for Covid-19 Detection using CT-Scans},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140372},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140372},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Nagwa G. Ali and Fahad K. El Sheref and Mahmoud M. El khouly}
}