COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion

In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 d...

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Autores principales:	Muhammad Attique Khan, Majed Alhaisoni, Usman Tariq, Nazar Hussain, Abdul Majid, Robertas Damaševičius, Rytis Maskeliūnas
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	COVID-19 deep learning feature fusion firefly algorithm medical imaging Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/835acbc92f9744119d1cabe56af80802
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id	oai:doaj.org-article:835acbc92f9744119d1cabe56af80802
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spelling	oai:doaj.org-article:835acbc92f9744119d1cabe56af808022021-11-11T19:14:36ZCOVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion10.3390/s212172861424-8220https://doaj.org/article/835acbc92f9744119d1cabe56af808022021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7286https://doaj.org/toc/1424-8220In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach—parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.Muhammad Attique KhanMajed AlhaisoniUsman TariqNazar HussainAbdul MajidRobertas DamaševičiusRytis MaskeliūnasMDPI AGarticleCOVID-19deep learningfeature fusionfirefly algorithmmedical imagingChemical technologyTP1-1185ENSensors, Vol 21, Iss 7286, p 7286 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 deep learning feature fusion firefly algorithm medical imaging Chemical technology TP1-1185
spellingShingle	COVID-19 deep learning feature fusion firefly algorithm medical imaging Chemical technology TP1-1185 Muhammad Attique Khan Majed Alhaisoni Usman Tariq Nazar Hussain Abdul Majid Robertas Damaševičius Rytis Maskeliūnas COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion
description	In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach—parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.
format	article
author	Muhammad Attique Khan Majed Alhaisoni Usman Tariq Nazar Hussain Abdul Majid Robertas Damaševičius Rytis Maskeliūnas
author_facet	Muhammad Attique Khan Majed Alhaisoni Usman Tariq Nazar Hussain Abdul Majid Robertas Damaševičius Rytis Maskeliūnas
author_sort	Muhammad Attique Khan
title	COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion
title_short	COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion
title_full	COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion
title_fullStr	COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion
title_full_unstemmed	COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion
title_sort	covid-19 case recognition from chest ct images by deep learning, entropy-controlled firefly optimization, and parallel feature fusion
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/835acbc92f9744119d1cabe56af80802
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COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion

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