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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3PJ5TCS
Repositorysid.inpe.br/sibgrapi/2017/09.04.22.16
Last Update2017:09.04.22.16.45 felipejordaopinheiro@gmail.com
Metadatasid.inpe.br/sibgrapi/2017/09.04.22.16.45
Metadata Last Update2020:02.20.22.06.47 administrator
Citation KeyAndradePaivSilv:2017:AnImTe
TitleAnálise de Imagens de Termografia Dinâmica para Classificação de Alterações na Mama Usando Séries Temporais
FormatOn-line
Year2017
Access Date2021, Jan. 25
Number of Files1
Size436 KiB
Context area
Author1 Andrade, Felipe Jordão Pinheiro de
2 Paiva, Anselmo Cardoso
3 Silva, Aristófanes Corrêa
Affiliation1 Universidade Federal do Maranhão
2 Universidade Federal do Maranhão
3 Universidade Federal do Maranhão
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressfelipejordaopinheiro@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
DateOct. 17-20, 2017
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeUndergraduate Work
History2017-09-04 22:16:45 :: felipejordaopinheiro@gmail.com -> administrator ::
2020-02-20 22:06:47 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsTermografia Dinâmica, Séries Temporais.
AbstractWith the increase in the number of cases of breast cancer in the last years, the need for auxiliary techniques for the detection of the disease is evident. Dynamic thermography can be used as an auxiliary method to the gold standard, the mammography screening. The thermography exam takes advantage of the fact that the lesions present a higher temperature than the healthy neighboring tissues. In this work, we propose a methodology for the transformation of thermal signals into time series, from which features for the classification task will be extracted. In the paper we compare the K-Star and Support Vector Machine classifiers with results of 95.8% accuracy, 93.6% sensitivity and 95.9% specificity.
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data URLhttp://urlib.net/rep/8JMKD3MGPAW/3PJ5TCS
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PJ5TCS
Languagept
Target FileCameraReady_Sibigrapi_termica.pdf
User Groupfelipejordaopinheiro@gmail.com
Visibilityshown
Update Permissionnot transferred
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PJT9LS
8JMKD3MGPAW/3PKCC58
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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