Classification of Alzheimer's Progression Using fMRI Data.
Sensors (Basel)
; 23(14)2023 Jul 12.
Article
em En
| MEDLINE
| ID: mdl-37514624
ABSTRACT
In the last three decades, the development of functional magnetic resonance imaging (fMRI) has significantly contributed to the understanding of the brain, functional brain mapping, and resting-state brain networks. Given the recent successes of deep learning in various fields, we propose a 3D-CNN-LSTM classification model to diagnose health conditions with the following classes condition normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD). The proposed method employs spatial and temporal feature extractors, wherein the former utilizes a U-Net architecture to extract spatial features, and the latter utilizes long short-term memory (LSTM) to extract temporal features. Prior to feature extraction, we performed four-step pre-processing to remove noise from the fMRI data. In the comparative experiments, we trained each of the three models by adjusting the time dimension. The network exhibited an average accuracy of 96.4% when using five-fold cross-validation. These results show that the proposed method has high potential for identifying the progression of Alzheimer's by analyzing 4D fMRI data.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Imageamento por Ressonância Magnética
/
Doença de Alzheimer
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2023
Tipo de documento:
Article