Pattern recognition with applications to pre-diagnosis of pathologies in the vocal tract
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1201/b18660-143 http://hdl.handle.net/11449/234382 |
Resumo: | For the detection of laryngeal pathologies, in general medical examinations, for example laryngoscopy and stroboscopy, are adopted. Besides being considered invasive and uncomfortable procedures, they are made only by medical request when the diseases are already on advanced levels. In order to perform a computational pre-diagnosis of such conditions, this paper presents a non-invasive technique in which three classifiers are tested and compared: Euclidian distance, RBF Neural Network with the Gaussian kernel, and RBF Neural Network with the modified Gaussian kernel. Based on a database of normal and pathological voices, tests that demonstrate the effectiveness of the proposed technique, which can be implemented in real-time, were performed. |
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Repositório Institucional da UNESP |
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Pattern recognition with applications to pre-diagnosis of pathologies in the vocal tractEuclidian distanceLarynx pathologiesRBF neural networksSignal processingFor the detection of laryngeal pathologies, in general medical examinations, for example laryngoscopy and stroboscopy, are adopted. Besides being considered invasive and uncomfortable procedures, they are made only by medical request when the diseases are already on advanced levels. In order to perform a computational pre-diagnosis of such conditions, this paper presents a non-invasive technique in which three classifiers are tested and compared: Euclidian distance, RBF Neural Network with the Gaussian kernel, and RBF Neural Network with the modified Gaussian kernel. Based on a database of normal and pathological voices, tests that demonstrate the effectiveness of the proposed technique, which can be implemented in real-time, were performed.University of São PauloNorth of São Paulo UniversitySão Paulo State UniversitySão Paulo State UniversityUniversidade de São Paulo (USP)Universidade Estadual Paulista (UNESP)Solgon Bassi, Regiane DeniseDezani, Henrique [UNESP]Silva Paulo, Kátia CristinaCapobianco Guido, Rodrigo [UNESP]Nunes da Silva, IvanMarranghello, Norian [UNESP]2022-05-02T13:30:31Z2022-05-02T13:30:31Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject645-648http://dx.doi.org/10.1201/b18660-143Network Security and Communication Engineering - Proceedings of the 2014 International Conference on Network Security and Communication Engineering, NSCE 2014, p. 645-648.http://hdl.handle.net/11449/23438210.1201/b18660-1432-s2.0-84961619752Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengNetwork Security and Communication Engineering - Proceedings of the 2014 International Conference on Network Security and Communication Engineering, NSCE 2014info:eu-repo/semantics/openAccess2022-05-02T13:30:32Zoai:repositorio.unesp.br:11449/234382Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:42:50.694317Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Pattern recognition with applications to pre-diagnosis of pathologies in the vocal tract |
title |
Pattern recognition with applications to pre-diagnosis of pathologies in the vocal tract |
spellingShingle |
Pattern recognition with applications to pre-diagnosis of pathologies in the vocal tract Solgon Bassi, Regiane Denise Euclidian distance Larynx pathologies RBF neural networks Signal processing |
title_short |
Pattern recognition with applications to pre-diagnosis of pathologies in the vocal tract |
title_full |
Pattern recognition with applications to pre-diagnosis of pathologies in the vocal tract |
title_fullStr |
Pattern recognition with applications to pre-diagnosis of pathologies in the vocal tract |
title_full_unstemmed |
Pattern recognition with applications to pre-diagnosis of pathologies in the vocal tract |
title_sort |
Pattern recognition with applications to pre-diagnosis of pathologies in the vocal tract |
author |
Solgon Bassi, Regiane Denise |
author_facet |
Solgon Bassi, Regiane Denise Dezani, Henrique [UNESP] Silva Paulo, Kátia Cristina Capobianco Guido, Rodrigo [UNESP] Nunes da Silva, Ivan Marranghello, Norian [UNESP] |
author_role |
author |
author2 |
Dezani, Henrique [UNESP] Silva Paulo, Kátia Cristina Capobianco Guido, Rodrigo [UNESP] Nunes da Silva, Ivan Marranghello, Norian [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Solgon Bassi, Regiane Denise Dezani, Henrique [UNESP] Silva Paulo, Kátia Cristina Capobianco Guido, Rodrigo [UNESP] Nunes da Silva, Ivan Marranghello, Norian [UNESP] |
dc.subject.por.fl_str_mv |
Euclidian distance Larynx pathologies RBF neural networks Signal processing |
topic |
Euclidian distance Larynx pathologies RBF neural networks Signal processing |
description |
For the detection of laryngeal pathologies, in general medical examinations, for example laryngoscopy and stroboscopy, are adopted. Besides being considered invasive and uncomfortable procedures, they are made only by medical request when the diseases are already on advanced levels. In order to perform a computational pre-diagnosis of such conditions, this paper presents a non-invasive technique in which three classifiers are tested and compared: Euclidian distance, RBF Neural Network with the Gaussian kernel, and RBF Neural Network with the modified Gaussian kernel. Based on a database of normal and pathological voices, tests that demonstrate the effectiveness of the proposed technique, which can be implemented in real-time, were performed. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-01-01 2022-05-02T13:30:31Z 2022-05-02T13:30:31Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1201/b18660-143 Network Security and Communication Engineering - Proceedings of the 2014 International Conference on Network Security and Communication Engineering, NSCE 2014, p. 645-648. http://hdl.handle.net/11449/234382 10.1201/b18660-143 2-s2.0-84961619752 |
url |
http://dx.doi.org/10.1201/b18660-143 http://hdl.handle.net/11449/234382 |
identifier_str_mv |
Network Security and Communication Engineering - Proceedings of the 2014 International Conference on Network Security and Communication Engineering, NSCE 2014, p. 645-648. 10.1201/b18660-143 2-s2.0-84961619752 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Network Security and Communication Engineering - Proceedings of the 2014 International Conference on Network Security and Communication Engineering, NSCE 2014 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
645-648 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129108706590720 |