Nour Eldeen Khalifa

Work place: Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt



Research Interests: Network Architecture, Computer Networks, Computer Architecture and Organization, Wireless Networks, Sensor, Network Security, Data Structures and Algorithms, Information-Theoretic Security


Nour Eldeen Khalifa received his B.Sc., M.Sc. and Ph.D. degree in 2006, 2009 and 2013 respectively, all from Cairo University, Faculty of Computers and Artificial Intelligence, Cairo, Egypt. He also had a Professional M.Sc. Degree in Cloud Computing in 2018. He authored/coauthored more than 30 publications and 2 edited books. He had more than 1400 citations. He reviewed several papers for international journals and conferences including (Scientific Reports, IEEE IoT, Neural Computing, and Artificial Intelligence Review). Currently, he is an associate professor at Faculty of Computers and Artificial Intelligence, Cairo University. His research interests include wireless sensor networks, cryptography, multimedia, network security, machine and deep learning.

Author Articles
A Model based on Deep Learning for COVID-19 X-rays Classification

By Eman I. Abd El-Latif Nour Eldeen Khalifa

DOI:, Pub. Date: 8 Feb. 2023

Throughout the COVID-19 pandemic in 2019 and until now, patients overrun hospitals and health care emergency units to check up on their health status. The health care systems were burdened by the increased number of patients and there was a need to speed up the diagnoses process of detecting this disease by using computer algorithms. In this paper, an integrated model based on deep and machine learning for covid-19 x-rays classification will be presented. The integration is built-up open two phases. The first phase is features extraction using deep transfer models such as Alexnet, Resnet18, VGG16, and VGG19. The second phase is the classification using machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, and Ensemble algorithm. The dataset selected consists of three classes (COVID-19, Viral pneumonia, and Normal) class and the dataset is available online under the name COVID-19 Radiography database. More than 30 experiments are conducted to select the optimal integration between machine and deep learning models. The integration of VGG19 and SVM achieved the highest accuracy possible with 98.61%. The performance indicators such as Recall, Precision, and F1 Score support this finding. The proposed model consumes less time and resources in the training process if it is compared to deep transfer models. Comparative results are con-ducted at the end of the research, and the proposed model overcomes related works which used the same dataset in terms of testing accuracy.

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