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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45BTS9E
Repositorysid.inpe.br/sibgrapi/2021/08.31.13.14
Last Update2021:08.31.13.14.58 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/08.31.13.14.58
Metadata Last Update2022:06.14.00.00.18 (UTC) administrator
DOI10.1109/SIBGRAPI54419.2021.00058
Citation KeyRoderRosaPapaPedr:2021:EnShNe
TitleEnhancing Shallow Neural Networks Through Fourier-based Information Fusion for Stroke Classification
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size1288 KiB
2. Context
Author1 Roder, Mateus
2 Rosa, Gustavo Henrique
3 Papa, João Paulo
4 Pedronette, Daniel Carlos Guimarães
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addressmateus.roder@unesp.br
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2021-08-31 13:14:58 :: mateus.roder@unesp.br -> administrator ::
2022-03-02 00:54:15 :: administrator -> menottid@gmail.com :: 2021
2022-03-02 13:35:50 :: menottid@gmail.com -> administrator :: 2021
2022-06-14 00:00:18 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsStroke classification
Restricted Boltzmann Machines
Fourier transformation
AbstractDeep learning techniques have been widely researched and applied to several problems, ranging from recommendation systems and service-based analysis to medical diagnosis. Nevertheless, even with outstanding results in some computer vision tasks, there is still much to explore as problems are becoming more complex, or applications are demanding new restrictions that hamper current techniques performance. Several works have been developed throughout the last decade to support automated medical diagnosis, yet detecting neural-based strokes, the so-called cerebrovascular accident (CVA). However, such approaches have room for improvement, such as the employment of information fusion techniques in deep learning architectures. Such an approach might benefit CVA detection as most state-of-the-art models use computer-based tomography and magnetic resonance imaging samples. Therefore, the present work aims at enhancing stroke detection through information fusion, mainly composed of original and Fourier-based samples, applied to shallow architectures (Restricted Boltzmann machines). The whole picture employs multimodal inputs, allowing data from different domains (images and Fourier transforms) to be learned together, improving the model's predictive capacity. As the main result, the proposed approach overpassed the baselines, achieving the remarkable accuracy of 99.72%.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2021 > Enhancing Shallow Neural...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Enhancing Shallow Neural...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45BTS9E
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45BTS9E
Languageen
Target FilePaper ID 12.pdf
User Groupmateus.roder@unesp.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsaffiliation archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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