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Article

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

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Department of the Robotics and Intelligent Machines, Kafrelsheikh University, Kafrelsheikh 33511, Egypt
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Electrical Engineering Department, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
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School of Computing, Tokyo Institute of Technology, Yokohama 226-8502, Japan
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School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
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Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 32511, Egypt
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Centre for Excellence in Cybersecurity, Quantum Information Processing, and Artificial Intelligence, Menoufia University, Shebin El-Koom 32511, Egypt
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Medical Imaging and Interventional Radiology Departement, National Liver Institute, Menoufia university, Shebin El-Koom 32511, Egypt
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Public Health and Community Medicine Department, Faculty of Medicine Menoufia University, Shebin El-Koom 32511, Egypt
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Information Technology Department, Faculty of Computers and Information, Menoufia University, Shebin El-Koom 32511, Egypt
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School of Data Science and Technology, Heilongjiang University, Harbin 150080, China
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Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufa University, Menouf 32952, Egypt
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School of Information Technology and Computer Science, Nile University, Giza 12588, Egypt
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Authors to whom correspondence should be addressed.
Received: 26 April 2020 / Revised: 1 July 2020 / Accepted: 14 July 2020 / Published: 16 July 2020
(This article belongs to the Special Issue Virus Bioinformatics 2020)
This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus. View Full-Text
Keywords: COVID-19; Corona virus; machine learning; deep learning; CNN; LSTM networks; image processing COVID-19; Corona virus; machine learning; deep learning; CNN; LSTM networks; image processing
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MDPI and ACS Style

Sedik, A.; Iliyasu, A.M.; Abd El-Rahiem, B.; Abdel Samea, M.E.; Abdel-Raheem, A.; Hammad, M.; Peng, J.; Abd El-Samie, F.E.; Abd El-Latif, A.A. Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections. Viruses 2020, 12, 769. https://0-doi-org.brum.beds.ac.uk/10.3390/v12070769

AMA Style

Sedik A, Iliyasu AM, Abd El-Rahiem B, Abdel Samea ME, Abdel-Raheem A, Hammad M, Peng J, Abd El-Samie FE, Abd El-Latif AA. Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections. Viruses. 2020; 12(7):769. https://0-doi-org.brum.beds.ac.uk/10.3390/v12070769

Chicago/Turabian Style

Sedik, Ahmed; Iliyasu, Abdullah M.; Abd El-Rahiem, Basma; Abdel Samea, Mohammed E.; Abdel-Raheem, Asmaa; Hammad, Mohamed; Peng, Jialiang; Abd El-Samie, Fathi E.; Abd El-Latif, Ahmed A. 2020. "Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections" Viruses 12, no. 7: 769. https://0-doi-org.brum.beds.ac.uk/10.3390/v12070769

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