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

被引：0

作者：

Ali, Nagwa G. ^{[1
]}

El Sheref, Fahad K. ^{[2
]}

El Khouly, Mahmoud M. ^{[1
]}

机构：

[1] Helwan Univ, Fac Comp & Artificial Intelligence, Dept Informat Technol, Cairo, Egypt

[2] Beni Suef Univ, Fac Comp & Artificial Intelligence, Dept Informat Syst, Bani Suwayf, Egypt

来源：

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS | 2023年 / 14卷 / 03期

关键词：

Covid-19; coronavirus; transfer-learning; CT-scan and ensemble method;

D O I：

10.14569/IJACSA.2023.0140372

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：627 / 633

页数：7

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