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Automatic RNA virus classification using the Entropy-ANFIS method
Innovations in the fields of medicine and medical image processing are becoming increasingly important. Historically, RNA viruses produced in cell cultures have been identified using electron microscopy, in which virus identification is performed by eye. Such an approach is time consuming and depend...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7173157/ https://www.ncbi.nlm.nih.gov/pubmed/32336901 http://dx.doi.org/10.1016/j.dsp.2013.01.011 |
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author | Dogantekin, Esin Avci, Engin Erkus, Oznur |
author_facet | Dogantekin, Esin Avci, Engin Erkus, Oznur |
author_sort | Dogantekin, Esin |
collection | PubMed |
description | Innovations in the fields of medicine and medical image processing are becoming increasingly important. Historically, RNA viruses produced in cell cultures have been identified using electron microscopy, in which virus identification is performed by eye. Such an approach is time consuming and depends on manual controls. Moreover, detailed knowledge about RNA viruses is required. This study introduces the Entropy-Adaptive Network Based Fuzzy Inference System (Entropy-ANFIS method), which can be used to automatically detect RNA virus images. This system consists of four stages: pre-processing, feature extraction, classification and testing the Entropy-ANFIS method with respect to the correct classification ratio. In the pre-processing stage, a center-edge changing method is used, in which the Euclidian distances are calculated from the center pixels to the edges of the imaged object. In this way, the distance vector is obtained. This calculation is repeated for each RNA virus image. In feature extraction, stage norm entropy, logarithmic energy and threshold entropy values are calculated to form the feature vector. The obtained feature vector is independent of the rotation and scale of the RNA virus image. In the classification stage, the feature vector is given as input to the ANFIS classifier, ANN classifier and FCM cluster. Finally, the test stage is performed to evaluate the correct classification ratio of the Entropy-ANFIS algorithm for the RNA virus images. The correct classification ratio has been determined as 95.12% using the proposed Entropy-ANFIS method. |
format | Online Article Text |
id | pubmed-7173157 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71731572020-04-22 Automatic RNA virus classification using the Entropy-ANFIS method Dogantekin, Esin Avci, Engin Erkus, Oznur Digit Signal Process Article Innovations in the fields of medicine and medical image processing are becoming increasingly important. Historically, RNA viruses produced in cell cultures have been identified using electron microscopy, in which virus identification is performed by eye. Such an approach is time consuming and depends on manual controls. Moreover, detailed knowledge about RNA viruses is required. This study introduces the Entropy-Adaptive Network Based Fuzzy Inference System (Entropy-ANFIS method), which can be used to automatically detect RNA virus images. This system consists of four stages: pre-processing, feature extraction, classification and testing the Entropy-ANFIS method with respect to the correct classification ratio. In the pre-processing stage, a center-edge changing method is used, in which the Euclidian distances are calculated from the center pixels to the edges of the imaged object. In this way, the distance vector is obtained. This calculation is repeated for each RNA virus image. In feature extraction, stage norm entropy, logarithmic energy and threshold entropy values are calculated to form the feature vector. The obtained feature vector is independent of the rotation and scale of the RNA virus image. In the classification stage, the feature vector is given as input to the ANFIS classifier, ANN classifier and FCM cluster. Finally, the test stage is performed to evaluate the correct classification ratio of the Entropy-ANFIS algorithm for the RNA virus images. The correct classification ratio has been determined as 95.12% using the proposed Entropy-ANFIS method. Elsevier Inc. 2013-07 2013-01-29 /pmc/articles/PMC7173157/ /pubmed/32336901 http://dx.doi.org/10.1016/j.dsp.2013.01.011 Text en Copyright © 2013 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 Dogantekin, Esin Avci, Engin Erkus, Oznur Automatic RNA virus classification using the Entropy-ANFIS method |
title | Automatic RNA virus classification using the Entropy-ANFIS method |
title_full | Automatic RNA virus classification using the Entropy-ANFIS method |
title_fullStr | Automatic RNA virus classification using the Entropy-ANFIS method |
title_full_unstemmed | Automatic RNA virus classification using the Entropy-ANFIS method |
title_short | Automatic RNA virus classification using the Entropy-ANFIS method |
title_sort | automatic rna virus classification using the entropy-anfis method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7173157/ https://www.ncbi.nlm.nih.gov/pubmed/32336901 http://dx.doi.org/10.1016/j.dsp.2013.01.011 |
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