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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2021
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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%. |
format | Online Article Text |
id | pubmed-8431962 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
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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