Vol.28, No.2, 2021, pp.293-303, doi:10.32604/iasc.2021.014419
OPEN ACCESS
ARTICLE
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:
(This article belongs to this Special Issue: Computational Intelligence for Internet of Medical Things and Big Data Analytics)
Received 19 September 2020; Accepted 23 January 2021; Issue published 01 April 2021
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.
Keywords
COVID-19; deep learning; convolution neural network; X-ray
Cite This Article
S. I., B. Al-Khateeb, M. Mahmood and B. Garcia-Zapirain, "An enhanced convolutional neural network for covid-19 detection," Intelligent Automation & Soft Computing, vol. 28, no.2, pp. 293–303, 2021.
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