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A benchmark for Rey-Osterrieth complex figure test automatic scoring

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dc.contributor.authorGuerrero Martín, Juan
dc.contributor.authorDíaz Mardomingo, María del Carmen
dc.contributor.authorGarcía Herranz, Sara
dc.contributor.authorMartínez Tomás, Rafael
dc.contributor.authorRincón Zamorano, Mariano
dc.date.accessioned2024-12-02T13:23:59Z
dc.date.available2024-12-02T13:23:59Z
dc.date.issued2024-10-29
dc.descriptionEste es el manuscrito aceptado del artículo. La versión registrada fue publicada por primera vez en Heliyon,(2024) 10, 21, 2024, e39883, está disponible en línea en el sitio web del editor: https://doi.org/10.1016/j.heliyon.2024.e39883 This is the accepted manuscript of the article. The registered version was first published in Heliyon,(2024) 10, 21, 2024, e39883, is available online at the publisher's website: https://doi.org/10.1016/j.heliyon.2024.e39883
dc.description.abstractThe Rey–Osterrieth complex figure (ROCF) test is a neuropsychological task that can be useful for early detection of cognitive decline in the elderly population. Several computer vision systems have been proposed to automate this complex analysis task, but the lack of public benchmarks does not allow a fair comparison of these systems. To advance in that direction, we present a benchmarking framework for the automatic scoring of the ROCF test that provides: the ROCFD528 dataset, which is the first open dataset of ROCF line drawings; and experimental results obtained by several modern deep learning models, which can be used as a baseline for comparing new proposals. We evaluate different state-of-the-art convolutional neural networks (CNNs) under traditional and transfer learning paradigms. Experimental quantitative results (MAE = 3.448) indicate that a CNN specifically designed for sketches outperforms other state of the art CNN architectures when the number of examples available is limited. This benchmark can also be a paradigmatic example within the broad field of machine learning for the development of efficient and robust models for analyzing line drawings and sketches not only in classification but also in regression tasks.en
dc.description.versionversión publicada
dc.identifier.citationJuan Guerrero-Martín, María del Carmen Díaz-Mardomingo, Sara García-Herranz, Rafael Martínez-Tomás, Mariano Rincón, A benchmark for Rey-Osterrieth complex figure test automatic scoring, Heliyon, Volume 10, Issue 21, 2024, e39883, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2024.e39883
dc.identifier.doihttps://doi.org/10.1016/j.heliyon.2024.e39883
dc.identifier.issn2405-8440
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24644
dc.journal.issue21
dc.journal.titleHeliyon
dc.journal.volume10
dc.language.isoen
dc.page.initiale39883
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.relation.isreferencedbyhttps://edatos.consorciomadrono.es/dataverse/rey
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject61 Psicología::6105 Evaluación y diagnóstico en psicología
dc.subject.keywordsBenchmarken
dc.subject.keywordsRey-Osterrieth complex figure scoringen
dc.subject.keywordsDeep learningen
dc.subject.keywordsTransfer learningen
dc.subject.keywordsCognitive impairment detectionen
dc.titleA benchmark for Rey-Osterrieth complex figure test automatic scoringen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscovery7021a342-1a4f-4a9a-ba38-b31870e8e2f4
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