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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/49JP76P
Repositorysid.inpe.br/sibgrapi/2023/08.07.20.05
Last Update2023:08.07.20.05.21 (UTC) mateus.roder@unesp.br
Metadata Repositorysid.inpe.br/sibgrapi/2023/08.07.20.05.21
Metadata Last Update2024:02.17.04.05.20 (UTC) administrator
DOI10.1109/SIBGRAPI59091.2023.10347165
Citation KeyRoderGomYosCosPap:2023:MuCoDe
TitleMultimodal Convolutional Deep Belief Networks for Stroke Classification with Fourier Transform
FormatOn-line
Year2023
Access Date2024, Apr. 28
Number of Files1
Size877 KiB
2. Context
Author1 Roder, Mateus
2 Gomes, Nicolas
3 Yoshida, Arissa
4 Costen, Fumie
5 Papa, João Paulo
Affiliation1 São Paulo State University (UNESP)
2 São Paulo State University (UNESP)
3 São Paulo State University (UNESP)
4 The University of Manchester
5 São Paulo State University (UNESP)
EditorClua, Esteban Walter Gonzalez
Körting, Thales Sehn
Paulovich, Fernando Vieira
Feris, Rogerio
e-Mail Addressmateus.roder@unesp.br
Conference NameConference on Graphics, Patterns and Images, 36 (SIBGRAPI)
Conference LocationRio Grande, RS
DateNov. 06-09, 2023
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2023-08-07 20:05:21 :: mateus.roder@unesp.br -> administrator ::
2024-02-17 04:05:20 :: administrator -> mateus.roder@unesp.br :: 2023
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsStroke classification
Convolutional Deep Belief Network
RBM
Fourier transform
AbstractSeveral studies have investigated the vast potential of deep learning techniques in addressing a wide range of applications, from recommendation systems and service-based analysis to medical diagnosis. However, even with the remarkable results achieved in some computer vision tasks, there is still a vast scope for exploration. Over the past decade, various studies focused on developing automated medical systems to support diagnosis. Nevertheless, detecting cerebrovascular accidents remains a challenging task. In this regard, one way to improve these approaches is to incorporate information fusion techniques in deep learning architectures. This paper proposes a novel approach to enhance stroke classification by combining multimodal data from Fourier transform with Convolutional Deep Belief Networks. As the main result, the proposed approach achieved state-of-the-art results with an accuracy of 99.94%, demonstrating its effectiveness and potential for future applications.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/49JP76P
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/49JP76P
Languageen
Target Fileroder-inpe.pdf
User Groupmateus.roder@unesp.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
7. Description control
e-Mail (login)mateus.roder@unesp.br
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