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Classification of Covid-19 coronavirus, pneumonia and healthy lungs in CT scans using Q-deformed entropy and deep learning features

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. T...

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Main Authors: Hasan, Ali M., AL-Jawad, Mohammed M., Jalab, Hamid A., Shaiba, Hadil, Ibrahim, Rabha W., AL-Shamasneh, Ala'a R.
Format: Article
Published: MDPI 2020
Subjects:
QA75 Electronic computers. Computer science
Medical technology
Online Access:http://eprints.um.edu.my/36720/
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spelling my.um.eprints.367202024-11-05T07:31:20Z http://eprints.um.edu.my/36720/ Classification of Covid-19 coronavirus, pneumonia and healthy lungs in CT scans using Q-deformed entropy and deep learning features Hasan, Ali M. AL-Jawad, Mohammed M. Jalab, Hamid A. Shaiba, Hadil Ibrahim, Rabha W. AL-Shamasneh, Ala'a R. QA75 Electronic computers. Computer science Medical technology 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%. MDPI 2020-05 Article PeerReviewed Hasan, Ali M. and AL-Jawad, Mohammed M. and Jalab, Hamid A. and Shaiba, Hadil and Ibrahim, Rabha W. and AL-Shamasneh, Ala'a R. (2020) Classification of Covid-19 coronavirus, pneumonia and healthy lungs in CT scans using Q-deformed entropy and deep learning features. Entropy, 22 (5). ISSN 1099-4300, DOI https://doi.org/10.3390/E22050517 <https://doi.org/10.3390/E22050517>. 10.3390/E22050517
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
Medical technology
spellingShingle QA75 Electronic computers. Computer science
Medical technology
Hasan, Ali M.
AL-Jawad, Mohammed M.
Jalab, Hamid A.
Shaiba, Hadil
Ibrahim, Rabha W.
AL-Shamasneh, Ala'a R.
Classification of Covid-19 coronavirus, pneumonia and healthy lungs in CT scans using Q-deformed entropy and deep learning features
description 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%.
format Article
author Hasan, Ali M.
AL-Jawad, Mohammed M.
Jalab, Hamid A.
Shaiba, Hadil
Ibrahim, Rabha W.
AL-Shamasneh, Ala'a R.
author_facet Hasan, Ali M.
AL-Jawad, Mohammed M.
Jalab, Hamid A.
Shaiba, Hadil
Ibrahim, Rabha W.
AL-Shamasneh, Ala'a R.
author_sort Hasan, Ali M.
title Classification of Covid-19 coronavirus, pneumonia and healthy lungs in CT scans using Q-deformed entropy and deep learning features
title_short Classification of Covid-19 coronavirus, pneumonia and healthy lungs in CT scans using Q-deformed entropy and deep learning features
title_full Classification of Covid-19 coronavirus, pneumonia and healthy lungs in CT scans using Q-deformed entropy and deep learning features
title_fullStr Classification of Covid-19 coronavirus, pneumonia and healthy lungs in CT scans using Q-deformed entropy and deep learning features
title_full_unstemmed Classification of Covid-19 coronavirus, pneumonia and healthy lungs in CT scans using Q-deformed entropy and deep learning features
title_sort classification of covid-19 coronavirus, pneumonia and healthy lungs in ct scans using q-deformed entropy and deep learning features
publisher MDPI
publishDate 2020
url http://eprints.um.edu.my/36720/
_version_ 1814933262612561920
score 13.211869

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