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

Autor:	Ala'a R. Al-Shamasneh, Hamid A. Jalab, Rabha W. Ibrahim, Mohammed M Al-Jawad, Hadil Shaiba, Ali M. Hasan
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	Coronavirus disease 2019 (COVID-19) Computer science Feature extraction General Physics and Astronomy lcsh:Astrophysics 02 engineering and technology fractional calculus medicine.disease_cause Article 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Histogram lcsh:QB460-466 0202 electrical engineering electronic engineering information engineering medicine Limited capacity Entropy (information theory) lcsh:Science Coronavirus business.industry Deep learning CT scans of lungs deep learning Pattern recognition Thresholding lcsh:QC1-999 features extraction 020201 artificial intelligence & image processing lcsh:Q Artificial intelligence Q—deformed entropy business LSTM network lcsh:Physics
Zdroj:	Entropy Entropy, Vol 22, Iss 517, p 517 (2020) Volume 22 Issue 5
ISSN:	1099-4300
Popis:	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&rsquo 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%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8453e831c909991c7f3ed724da665cdf http://europepmc.org/articles/PMC7517011 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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