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A novel machine learning–based detection and diagnosis model for coronavirus disease (COVID-19) using discrete wavelet transform with rough neural network
Coronavirus disease-2019 (COVID-19) screening testing of a massive number of suspected cases for proper quarantine and treatment is a priority. Pathogenic laboratory testing is used as a diagnosis process, but it consumes much time with a high false detection rate. The development of artificial inte...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138000/ http://dx.doi.org/10.1016/B978-0-12-824536-1.00009-5 |
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author | Pustokhina, Irina Valeryevna Pustokhin, Denis Alexandrovich Shankar, K. |
author_facet | Pustokhina, Irina Valeryevna Pustokhin, Denis Alexandrovich Shankar, K. |
author_sort | Pustokhina, Irina Valeryevna |
collection | PubMed |
description | Coronavirus disease-2019 (COVID-19) screening testing of a massive number of suspected cases for proper quarantine and treatment is a priority. Pathogenic laboratory testing is used as a diagnosis process, but it consumes much time with a high false detection rate. The development of artificial intelligence techniques has a crucial part in streamlining and accelerating the diagnosis of COVID-19 patients. To meet current requirements for COVID-19 diagnosis, it is highly important to develop an automated diagnosis model to identify the disease accurately and at a faster rate. According to COVID-19 radiographical changes in computed tomography images, this research designed a machine learning–based diagnosis model using discrete wavelet transform (DWT) with a rough neural network (RNN), called a DWT-RNN model. Moreover, principal component analysis was conducted to reduce the subset of features before classification. The DWT-RNN model was validated using a COVID-19 chest X-ray dataset. The experimental outcome was validated for several aspects and the obtained results show that the DWT-RNN model offers maximum classification performance and saves time in controlling diseases. |
format | Online Article Text |
id | pubmed-8138000 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-81380002021-05-21 A novel machine learning–based detection and diagnosis model for coronavirus disease (COVID-19) using discrete wavelet transform with rough neural network Pustokhina, Irina Valeryevna Pustokhin, Denis Alexandrovich Shankar, K. Data Science for COVID-19 Article Coronavirus disease-2019 (COVID-19) screening testing of a massive number of suspected cases for proper quarantine and treatment is a priority. Pathogenic laboratory testing is used as a diagnosis process, but it consumes much time with a high false detection rate. The development of artificial intelligence techniques has a crucial part in streamlining and accelerating the diagnosis of COVID-19 patients. To meet current requirements for COVID-19 diagnosis, it is highly important to develop an automated diagnosis model to identify the disease accurately and at a faster rate. According to COVID-19 radiographical changes in computed tomography images, this research designed a machine learning–based diagnosis model using discrete wavelet transform (DWT) with a rough neural network (RNN), called a DWT-RNN model. Moreover, principal component analysis was conducted to reduce the subset of features before classification. The DWT-RNN model was validated using a COVID-19 chest X-ray dataset. The experimental outcome was validated for several aspects and the obtained results show that the DWT-RNN model offers maximum classification performance and saves time in controlling diseases. 2021 2021-05-21 /pmc/articles/PMC8138000/ http://dx.doi.org/10.1016/B978-0-12-824536-1.00009-5 Text en Copyright © 2021 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Pustokhina, Irina Valeryevna Pustokhin, Denis Alexandrovich Shankar, K. A novel machine learning–based detection and diagnosis model for coronavirus disease (COVID-19) using discrete wavelet transform with rough neural network |
title | A novel machine learning–based detection and diagnosis model for coronavirus disease (COVID-19) using discrete wavelet transform with rough neural network |
title_full | A novel machine learning–based detection and diagnosis model for coronavirus disease (COVID-19) using discrete wavelet transform with rough neural network |
title_fullStr | A novel machine learning–based detection and diagnosis model for coronavirus disease (COVID-19) using discrete wavelet transform with rough neural network |
title_full_unstemmed | A novel machine learning–based detection and diagnosis model for coronavirus disease (COVID-19) using discrete wavelet transform with rough neural network |
title_short | A novel machine learning–based detection and diagnosis model for coronavirus disease (COVID-19) using discrete wavelet transform with rough neural network |
title_sort | novel machine learning–based detection and diagnosis model for coronavirus disease (covid-19) using discrete wavelet transform with rough neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138000/ http://dx.doi.org/10.1016/B978-0-12-824536-1.00009-5 |
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