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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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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. |
format | Online Article Text |
id | pubmed-8158467 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
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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