Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyAfonsoPerWebHooPap:2017:PaDiId
TitleParkinson's Disease Identification Through Deep Optimum-Path Forest Clustering
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size1057 KiB
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Author1 Afonso, Luis Claudio Sugi
2 Pereira, Clayton Reginaldo
3 Weber, Silke Anna Theresa
4 Hook, Christian
5 Papa, João Paulo
Affiliation1 UFSCar - Federal University of São Carlos, Department of Computing, São Carlos, Brazil
2 UFSCar - Federal University of São Carlos, Department of Computing, São Carlos, Brazil
3 UNESP - São Paulo State University, Medical School, Botucatu, Brazil
4 Ostbayerische Tech. Hochschule, Fakultat Informatik/Mathematik, Regensburg, Germany
5 UNESP - São Paulo State University, School of Sciences, Bauru, Brazil
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
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-18 05:36:24 :: -> administrator ::
2020-02-19 02:01:23 :: administrator -> :: 2017
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsParkinson's disease, Optimum-Path Forest, Handwriting Dynamics.
AbstractApproximately 50,000 to 60,000 new cases of Parkinson's disease (PD) are diagnosed yearly. Despite being non-lethal, PD shortens life expectancy of the ones affected with such disease. As such, researchers from different fields of study have put great effort in order to develop methods aiming the identification of PD in its early stages. This work uses handwriting dynamics data acquired by a series of tasks and proposes the application of a deep-driven graph-based clustering algorithm known as Optimum-Path Forest to learn a dictionary-like representation of each individual in order to automatic identify Parkinson's disease. Experimental results have shown promising results, with results comparable to some state-of-the-art approaches in the literature.
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Next Higher Units8JMKD3MGPAW/3PJT9LS
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