Publicación:
Mild Cognitive Impairment Detection from Rey-Osterrieth Complex Figure Copy Drawings Using a Contrastive Loss Siamese Neural Network

Vista sencilla de ítem
dc.contributor.authorGuerrero Martín, Juan
dc.contributor.authorEstella Nonay, Eladio
dc.contributor.authorBachiller Mayoral, Margarita
dc.contributor.authorRincón Zamorano, Mariano
dc.contributor.funderUniversidad Nacional de Educación a Distancia (UNED)
dc.date.accessioned2025-10-21T05:29:06Z
dc.date.available2025-10-21T05:29:06Z
dc.date.issued2025-10-15
dc.descriptionThis research has been supported by an FPI-UNED-2021 scholarship.
dc.description.abstractNeuropsychological tests, such as the Rey-Osterrieth complex figure (ROCF) test, help detect mild cognitive impairment (MCI) in adults by assessing cognitive abilities such as planning, organization, and memory. Furthermore, they are inexpensive and minimally invasive, making them excellent tools for early screening. In this paper, we propose the use of image analysis models to characterize the relationship between an individual’s ROCF drawing and their cognitive state.This task is usually framed as a classification problem and is solved using deep learning models, due to their success in the last decade. In order to achieve good performance, these models need to be trained with a large number of examples. Given that our data availability is limited, we alternatively treat our task as a similarity learning problem, performing pairwise ROCF drawing comparisons to define groups that represent different cognitive states.This way of working could lead to better data utilization and improved model performance. To solve the similarity learning problem, we propose a siamese neural network (SNN) that exploits the distances of arbitrary ROCF drawings to the ideal representation of the ROCF. Our proposal is compared against various deep learning models designed for classification using a public dataset of 528 ROCF copy drawings, which are associated with either healthy individuals or those with MCI. Quantitative results are derived from a scheme involving multiple rounds of evaluation, employing both a dedicated test set and 14-fold cross-validation. Our SNN proposal demonstrates superiority in validation performance, and test results comparable to those of the classification-based deep learning models.en
dc.description.versionversión publicada
dc.identifier.doihttps://doi.org/10.32604/cmc.2025.066083
dc.identifier.issn1546-2226
dc.identifier.urihttps://hdl.handle.net/20.500.14468/30538
dc.journal.titleComputers, Materials & Continua
dc.journal.volumeOnline first
dc.language.isoen
dc.publisherTech Science Press
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/4.0/deed.es
dc.subject1203.04 Inteligencia artificial
dc.subject.keywordsMild cognitive impairment detectionen
dc.subject.keywordsRey-Osterrieth complex figureen
dc.subject.keywordsdeep learningen
dc.subject.keywordssiamese neural networken
dc.titleMild Cognitive Impairment Detection from Rey-Osterrieth Complex Figure Copy Drawings Using a Contrastive Loss Siamese Neural Networken
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication7021a342-1a4f-4a9a-ba38-b31870e8e2f4
relation.isAuthorOfPublication124f1ca1-9e71-43bb-b992-4a9c3997dc5d
relation.isAuthorOfPublication06a389cc-435e-4713-a8d7-8350f08ea1a8
relation.isAuthorOfPublication.latestForDiscovery7021a342-1a4f-4a9a-ba38-b31870e8e2f4
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
Guerrero_Martin_Juan_ReySiamese_JUAN GUERRERO MARTIN.pdf
Tamaño:
28.79 MB
Formato:
Adobe Portable Document Format
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
3.62 KB
Formato:
Item-specific license agreed to upon submission
Descripción:
Descargar
Colecciones
Artículos y papers