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Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as...

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Autores principales: Shankar, K., Perumal, Eswaran, Tiwari, Prayag, Shorfuzzaman, Mohammad, Gupta, Deepak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8158467/
https://www.ncbi.nlm.nih.gov/pubmed/34075280
http://dx.doi.org/10.1007/s00530-021-00800-x
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author Shankar, K.
Perumal, Eswaran
Tiwari, Prayag
Shorfuzzaman, Mohammad
Gupta, Deepak
author_facet Shankar, K.
Perumal, Eswaran
Tiwari, Prayag
Shorfuzzaman, Mohammad
Gupta, Deepak
author_sort Shankar, K.
collection PubMed
description In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as the bilateral modifications are considered an ill-posed problem. This paper presents a new metaheuristic-based fusion model for COVID-19 diagnosis using chest X-ray images. The proposed model comprises different preprocessing, feature extraction, and classification processes. Initially, the Weiner filtering (WF) technique is used for the preprocessing of images. Then, the fusion-based feature extraction process takes place by the incorporation of gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRM), and local binary patterns (LBP). Afterward, the salp swarm algorithm (SSA) selected the optimal feature subset. Finally, an artificial neural network (ANN) is applied as a classification process to classify infected and healthy patients. The proposed model's performance has been assessed using the Chest X-ray image dataset, and the results are examined under diverse aspects. The obtained results confirmed the presented model's superior performance over the state of art methods.
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spelling pubmed-81584672021-05-28 Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images Shankar, K. Perumal, Eswaran Tiwari, Prayag Shorfuzzaman, Mohammad Gupta, Deepak Multimed Syst Special Issue Paper In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as the bilateral modifications are considered an ill-posed problem. This paper presents a new metaheuristic-based fusion model for COVID-19 diagnosis using chest X-ray images. The proposed model comprises different preprocessing, feature extraction, and classification processes. Initially, the Weiner filtering (WF) technique is used for the preprocessing of images. Then, the fusion-based feature extraction process takes place by the incorporation of gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRM), and local binary patterns (LBP). Afterward, the salp swarm algorithm (SSA) selected the optimal feature subset. Finally, an artificial neural network (ANN) is applied as a classification process to classify infected and healthy patients. The proposed model's performance has been assessed using the Chest X-ray image dataset, and the results are examined under diverse aspects. The obtained results confirmed the presented model's superior performance over the state of art methods. Springer Berlin Heidelberg 2021-05-27 2022 /pmc/articles/PMC8158467/ /pubmed/34075280 http://dx.doi.org/10.1007/s00530-021-00800-x Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Special Issue Paper
Shankar, K.
Perumal, Eswaran
Tiwari, Prayag
Shorfuzzaman, Mohammad
Gupta, Deepak
Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images
title Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images
title_full Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images
title_fullStr Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images
title_full_unstemmed Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images
title_short Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images
title_sort deep learning and evolutionary intelligence with fusion-based feature extraction for detection of covid-19 from chest x-ray images
topic Special Issue Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8158467/
https://www.ncbi.nlm.nih.gov/pubmed/34075280
http://dx.doi.org/10.1007/s00530-021-00800-x
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