NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification.
IEEE Open J Eng Med Biol
; 5: 428-433, 2024.
Article
en En
| MEDLINE
| ID: mdl-38899023
ABSTRACT
Goal The purpose of this paper is to recognize autism spectrum disorders (ASD) using graph attention network. Methods:
we propose a node features graph attention network (NF-GAT) for learning functional connectivity (FC) features to achieve ASD diagnosis. Firstly, node features are modelled based on functional magnetic resonance imaging (fMRI) data, with each subject modelled as a graph. Next, we use the graph attention layer to learn the node features and gets the node information of different nodes for ASD classification.Results:
Compared with other models, the NF-GAT has significant advantages in terms of classification results.Conclusions:
NF-GAT can be effectively used for ASD classification.
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Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
IEEE Open J Eng Med Biol
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos