Facial Point Graphs for Amyotrophic Lateral Sclerosis Identification

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorRoyal Melbourne Institute of Technology (RMIT)-
Autor(es): dc.creatorGomes, Nicolas Barbosa-
Autor(es): dc.creatorYoshida, Arissa-
Autor(es): dc.creatorRoder, Mateus-
Autor(es): dc.creatorde Oliveira, Guilherme Camargo-
Autor(es): dc.creatorPapa, João Paulo-
Data de aceite: dc.date.accessioned2025-08-21T16:30:50Z-
Data de disponibilização: dc.date.available2025-08-21T16:30:50Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5220/0012428400003660-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307394-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307394-
Descrição: dc.descriptionIdentifying Amyotrophic Lateral Sclerosis (ALS) in its early stages is essential for establishing the beginning of treatment, enriching the outlook, and enhancing the overall well-being of those affected individuals. However, early diagnosis and detecting the disease’s signs is not straightforward. A simpler and cheaper way arises by analyzing the patient’s facial expressions through computational methods. When a patient with ALS engages in specific actions, e.g., opening their mouth, the movement of specific facial muscles differs from that observed in a healthy individual. This paper proposes Facial Point Graphs to learn information from the geometry of facial images to identify ALS automatically. The experimental outcomes in the Toronto Neuroface dataset show the proposed approach outperformed state-of-the-art results, fostering promising developments in the area.-
Descrição: dc.descriptionDepartment of Computing Sao Paulo State University (UNESP)-
Descrição: dc.descriptionSchool of Engineering Royal Melbourne Institute of Technology (RMIT)-
Descrição: dc.descriptionDepartment of Computing Sao Paulo State University (UNESP)-
Formato: dc.format207-214-
Idioma: dc.languageen-
Relação: dc.relationProceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectALS-
Palavras-chave: dc.subjectFacial Point Graph-
Palavras-chave: dc.subjectGraph Neural Networks-
Palavras-chave: dc.subjectNeurodegenerative Disease-
Título: dc.titleFacial Point Graphs for Amyotrophic Lateral Sclerosis Identification-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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