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dc.contributor.authorGalaz, Zoltan
dc.contributor.authorDrotar, Peter
dc.contributor.authorMekyska, Jiri
dc.contributor.authorGazda, Matej
dc.contributor.authorMucha, Jan
dc.contributor.authorZvoncak, Vojtech
dc.contributor.authorSmekal, Zdenek
dc.contributor.authorFaundez-Zanuy, Marcos
dc.contributor.authorCastrillon, Reinel
dc.contributor.authorOrozco-Arroyave, Juan Rafael
dc.contributor.authorRapcsak, Steven
dc.contributor.authorKincses, Tamas
dc.contributor.authorBrabenec, Lubos
dc.contributor.authorRektorova, Irena
dc.date.accessioned2023-11-14T10:48:54Z
dc.date.available2023-11-14T10:48:54Z
dc.date.issued2022
dc.identifier.citationGalaz Z, Drotar P, Mekyska J, Gazda M, Mucha J, Zvoncak V, Smekal Z, Faundez-Zanuy M, Castrillon R, Orozco-Arroyave JR, Rapcsak S, Kincses T, Brabenec L, Rektorova I. Comparison of CNN-learned vs. handcrafted features for detection of Parkinson's disease dysgraphia in a multilingual dataset. Front Neuroinform. 2022;(16):877139. DOI: 10.3389/fninf.2022.877139ca
dc.identifier.issn1662-5196ca
dc.identifier.urihttp://hdl.handle.net/20.500.12367/2482
dc.description.abstractParkinson’s disease dysgraphia (PDYS), one of the earliest signs of Parkinson’s disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive approach to monitoring the progress of the disease. However, although several approaches to supportive PDYS diagnosis have been proposed (mainly based on handcrafted features (HF) extracted from online handwriting or the utilization of deep neural networks), it remains unclear which approach provides the highest discrimination power and how these approaches can be transferred between different datasets and languages. This study aims to compare classification performance based on two types of features: features automatically extracted by a pretrained convolutional neural network (CNN) and HF designed by human experts. [...]ca
dc.format.extent18 p.ca
dc.publisherFrontiers Media S.A.ca
dc.relation.ispartofFrontiers in Neuroinformatics. 2022;(16):877139ca
dc.rights© 2022 Galaz, Drotar, Mekyska, Gazda, Mucha, Zvoncak, Smekal, Faundez-Zanuy, Castrillon, Orozco-Arroyave, Rapcsak, Kincses, Brabenec and Rektorova.ca
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.otherMachine learningca
dc.subject.otherDeep learningca
dc.subject.otherFeature extractionca
dc.subject.otherParkinson’s disease dysgraphiaca
dc.subject.otherHandwriting analysisca
dc.titleComparison of CNN-learned vs. handcrafted features for detection of Parkinson's disease dysgraphia in a multilingual datasetca
dc.typeinfo:eu-repo/semantics/articleca
dc.description.versioninfo:eu-repo/semantics/publishedVersionca
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscapca
dc.identifier.doi10.3389/fninf.2022.877139ca


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© 2022 Galaz, Drotar, Mekyska, Gazda, Mucha, Zvoncak, Smekal, Faundez-Zanuy, Castrillon, Orozco-Arroyave, Rapcsak, Kincses, Brabenec and Rektorova.
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/
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