Early diagnosis of Alzheimer's disease based on three-dimensional convolutional neural networks ensemble model combined with genetic algorithm / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 47-55, 2021.
Article
em Zh
| WPRIM
| ID: wpr-879248
Biblioteca responsável:
WPRO
ABSTRACT
The pathogenesis of Alzheimer's disease (AD), a common neurodegenerative disease, is still unknown. It is difficult to determine the atrophy areas, especially for patients with mild cognitive impairment (MCI) at different stages of AD, which results in a low diagnostic rate. Therefore, an early diagnosis model of AD based on 3-dimensional convolutional neural network (3DCNN) and genetic algorithm (GA) was proposed. Firstly, the 3DCNN was used to train a base classifier for each region of interest (ROI). And then, the optimal combination of the base classifiers was determined with the GA. Finally, the ensemble consisting of the chosen base classifiers was employed to make a diagnosis for a patient and the brain regions with significant classification capability were decided. The experimental results showed that the classification accuracy was 88.6% for AD
Palavras-chave
Texto completo:
1
Base de dados:
WPRIM
Assunto principal:
Encéfalo
/
Imageamento por Ressonância Magnética
/
Redes Neurais de Computação
/
Doenças Neurodegenerativas
/
Diagnóstico Precoce
/
Doença de Alzheimer
/
Disfunção Cognitiva
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Limite:
Humans
Idioma:
Zh
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2021
Tipo de documento:
Article