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Entropy
Volume 22
Issue 5
10.3390/e22050517
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Article

Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

by
Ali M. Hasan
1,
Mohammed M. AL-Jawad
2,
Hamid A. Jalab
3,*,
Hadil Shaiba
4,
Rabha W. Ibrahim
5,6 and
Ala’a R. AL-Shamasneh
3
1
College of Medicine, Al-Nahrain University, Baghdad 10001, Iraq
2
College of Science, Kerbala University, Kerbala 56001, Iraq
3
Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
4
Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 84428, Saudi Arabia
5
Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
6
Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(5), 517; https://doi.org/10.3390/e22050517
Received: 4 April 2020 / Revised: 28 April 2020 / Accepted: 28 April 2020 / Published: 1 May 2020
(This article belongs to the Special Issue Complex and Fractional Dynamical Systems)
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Abstract

Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists’ efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%. View Full-Text
Keywords: deep learning; CT scans of lungs; fractional calculus; Q—deformed entropy; features extraction; LSTM network deep learning; CT scans of lungs; fractional calculus; Q—deformed entropy; features extraction; LSTM network
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MDPI and ACS Style

Hasan, A.M.; AL-Jawad, M.M.; Jalab, H.A.; Shaiba, H.; Ibrahim, R.W.; AL-Shamasneh, A.R. Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features. Entropy 2020, 22, 517. https://doi.org/10.3390/e22050517

AMA Style

Hasan AM, AL-Jawad MM, Jalab HA, Shaiba H, Ibrahim RW, AL-Shamasneh AR. Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features. Entropy. 2020; 22(5):517. https://doi.org/10.3390/e22050517

Chicago/Turabian Style

Hasan, Ali M., Mohammed M. AL-Jawad, Hamid A. Jalab, Hadil Shaiba, Rabha W. Ibrahim, and Ala’a R. AL-Shamasneh. 2020. "Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features" Entropy 22, no. 5: 517. https://doi.org/10.3390/e22050517

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