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Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model

COVID-19 was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design eff...

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Autores principales: Shankar, K., Mohanty, Sachi Nandan, Yadav, Kusum, Gopalakrishnan, T., Elmisery, Ahmed M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8431962/
https://www.ncbi.nlm.nih.gov/pubmed/34522236
http://dx.doi.org/10.1007/s11571-021-09712-y
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author Shankar, K.
Mohanty, Sachi Nandan
Yadav, Kusum
Gopalakrishnan, T.
Elmisery, Ahmed M.
author_facet Shankar, K.
Mohanty, Sachi Nandan
Yadav, Kusum
Gopalakrishnan, T.
Elmisery, Ahmed M.
author_sort Shankar, K.
collection PubMed
description COVID-19 was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.
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spelling pubmed-84319622021-09-10 Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model Shankar, K. Mohanty, Sachi Nandan Yadav, Kusum Gopalakrishnan, T. Elmisery, Ahmed M. Cogn Neurodyn Research Article COVID-19 was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%. Springer Netherlands 2021-09-10 2023-06 /pmc/articles/PMC8431962/ /pubmed/34522236 http://dx.doi.org/10.1007/s11571-021-09712-y Text en © The Author(s), under exclusive licence to Springer Nature B.V. 2021
spellingShingle Research Article
Shankar, K.
Mohanty, Sachi Nandan
Yadav, Kusum
Gopalakrishnan, T.
Elmisery, Ahmed M.
Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model
title Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model
title_full Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model
title_fullStr Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model
title_full_unstemmed Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model
title_short Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model
title_sort automated covid-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8431962/
https://www.ncbi.nlm.nih.gov/pubmed/34522236
http://dx.doi.org/10.1007/s11571-021-09712-y
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