Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm

Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, the exploitatio...

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Autores principales:	Mohamed Abd Elaziz, Abdelghani Dahou, Naser A. Alsaleh, Ammar H. Elsheikh, Amal I. Saba, Mahmoud Ahmadein
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	feature selection metaheuristic atomic orbital search dynamic opposite-based learning Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/2d5c5487fe4e4498b855bab859d6c50b
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spelling	oai:doaj.org-article:2d5c5487fe4e4498b855bab859d6c50b2021-11-25T17:29:10ZBoosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm10.3390/e231113831099-4300https://doaj.org/article/2d5c5487fe4e4498b855bab859d6c50b2021-10-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1383https://doaj.org/toc/1099-4300Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, the exploitation of deep learning (DL) and optimization algorithms can be advantageous in early diagnosis and COVID-19 detection. In this paper, we propose a framework for COVID-19 images classification using hybridization of DL and swarm-based algorithms. The MobileNetV3 is used as a backbone feature extraction to learn and extract relevant image representations as a DL model. As a swarm-based algorithm, the Aquila Optimizer (Aqu) is used as a feature selector to reduce the dimensionality of the image representations and improve the classification accuracy using only the most essential selected features. To validate the proposed framework, two datasets with X-ray and CT COVID-19 images are used. The obtained results from the experiments show a good performance of the proposed framework in terms of classification accuracy and dimensionality reduction during the feature extraction and selection phases. The Aqu feature selection algorithm achieves accuracy better than other methods in terms of performance metrics.Mohamed Abd ElazizAbdelghani DahouNaser A. AlsalehAmmar H. ElsheikhAmal I. SabaMahmoud AhmadeinMDPI AGarticlefeature selectionmetaheuristicatomic orbital searchdynamic opposite-based learningScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1383, p 1383 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	feature selection metaheuristic atomic orbital search dynamic opposite-based learning Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	feature selection metaheuristic atomic orbital search dynamic opposite-based learning Science Q Astrophysics QB460-466 Physics QC1-999 Mohamed Abd Elaziz Abdelghani Dahou Naser A. Alsaleh Ammar H. Elsheikh Amal I. Saba Mahmoud Ahmadein Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm
description	Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, the exploitation of deep learning (DL) and optimization algorithms can be advantageous in early diagnosis and COVID-19 detection. In this paper, we propose a framework for COVID-19 images classification using hybridization of DL and swarm-based algorithms. The MobileNetV3 is used as a backbone feature extraction to learn and extract relevant image representations as a DL model. As a swarm-based algorithm, the Aquila Optimizer (Aqu) is used as a feature selector to reduce the dimensionality of the image representations and improve the classification accuracy using only the most essential selected features. To validate the proposed framework, two datasets with X-ray and CT COVID-19 images are used. The obtained results from the experiments show a good performance of the proposed framework in terms of classification accuracy and dimensionality reduction during the feature extraction and selection phases. The Aqu feature selection algorithm achieves accuracy better than other methods in terms of performance metrics.
format	article
author	Mohamed Abd Elaziz Abdelghani Dahou Naser A. Alsaleh Ammar H. Elsheikh Amal I. Saba Mahmoud Ahmadein
author_facet	Mohamed Abd Elaziz Abdelghani Dahou Naser A. Alsaleh Ammar H. Elsheikh Amal I. Saba Mahmoud Ahmadein
author_sort	Mohamed Abd Elaziz
title	Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm
title_short	Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm
title_full	Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm
title_fullStr	Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm
title_full_unstemmed	Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm
title_sort	boosting covid-19 image classification using mobilenetv3 and aquila optimizer algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2d5c5487fe4e4498b855bab859d6c50b
work_keys_str_mv	AT mohamedabdelaziz boostingcovid19imageclassificationusingmobilenetv3andaquilaoptimizeralgorithm AT abdelghanidahou boostingcovid19imageclassificationusingmobilenetv3andaquilaoptimizeralgorithm AT naseraalsaleh boostingcovid19imageclassificationusingmobilenetv3andaquilaoptimizeralgorithm AT ammarhelsheikh boostingcovid19imageclassificationusingmobilenetv3andaquilaoptimizeralgorithm AT amalisaba boostingcovid19imageclassificationusingmobilenetv3andaquilaoptimizeralgorithm AT mahmoudahmadein boostingcovid19imageclassificationusingmobilenetv3andaquilaoptimizeralgorithm
_version_	1718412275280248832

Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm

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