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An Enhanced Convolutional Neural Network for COVID-19 Detection

Sameer I. Ali Al-Janabi1, Belal Al-Khateeb2,*, Maha Mahmood2, Begonya Garcia-Zapirain3

1 College of Islamic Sciences, University of Anbar, Ramadi, Iraq
2 College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq
3 eVIDA Lab, University of Deusto, Avda/Universidades 24, 48007, Bilbao, Spain

* Corresponding Author: Belal Al-Khateeb. Email: email

(This article belongs to the Special Issue: Computational Intelligence for Internet of Medical Things and Big Data Analytics)

Intelligent Automation & Soft Computing 2021, 28(2), 293-303. https://doi.org/10.32604/iasc.2021.014419

Abstract

The recent novel coronavirus (COVID-19, as the World Health Organization has called it) has proven to be a source of risk for global public health. The virus, which causes an acute respiratory disease in persons, spreads rapidly and is now threatening more than 150 countries around the world. One of the essential procedures that patients with COVID-19 need is an accurate and rapid screening process. In this research, utilizing the features of deep learning methods, we present a method for detecting COVID-19 and a screening model that uses pulmonary computed tomography images to differentiate COVID-19 pneumonia from healthy cases. In this study, 256 cases (128 COVID-19, 128 normal) are used to detect COVID-19 early. Real cases of 51 external COVID-19 images are also taken from Iraqi hospitals and used to validate the proposed method. Segmentations of the lung and infection fields are retrieved from the images during preprocessing. The total accuracy obtained from the results is 98.70%, indicating the success of the designed model.

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APA Style
Al-Janabi, S.I.A., Al-Khateeb, B., Mahmood, M., Garcia-Zapirain, B. (2021). An enhanced convolutional neural network for COVID-19 detection. Intelligent Automation & Soft Computing, 28(2), 293-303. https://doi.org/10.32604/iasc.2021.014419
Vancouver Style
Al-Janabi SIA, Al-Khateeb B, Mahmood M, Garcia-Zapirain B. An enhanced convolutional neural network for COVID-19 detection. Intell Automat Soft Comput . 2021;28(2):293-303 https://doi.org/10.32604/iasc.2021.014419
IEEE Style
S.I.A. Al-Janabi, B. Al-Khateeb, M. Mahmood, and B. Garcia-Zapirain "An Enhanced Convolutional Neural Network for COVID-19 Detection," Intell. Automat. Soft Comput. , vol. 28, no. 2, pp. 293-303. 2021. https://doi.org/10.32604/iasc.2021.014419



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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