Application of 3D Whole-Brain Texture Analysis and the Feature Selection Method Based on within-Class Scatter in the Classification and Diagnosis of Alzheimer's Disease.
Ther Innov Regul Sci
; 56(4): 561-571, 2022 07.
Article
en En
| MEDLINE
| ID: mdl-35344200
ABSTRACT
BACKGROUND:
Patients with mild cognitive impairment (MCI) are a high-risk group for Alzheimer's disease (AD). Thus, a reliable prediction of the conversion from MCI to AD based on three-dimensional (3D) texture features of MRI images could help doctors in developing effective treatment protocols.METHODS:
The 3D texture features of the whole-brain were deduced based on the gray-level co-occurrence matrix. Then, the embedded feature selection method based on least squares loss and within-class scatter (LSWCS) was employed to select the optimal subsets of features that were used for binary classification (AD, MCI_C, MCI_S, normal control in pairs) based on SVM. A tenfold cross validation was repeated ten times for each classification. LASSO, fused_LASSO, and group LASSO are used in feature selection step for comparison.RESULTS:
The accuracy and the selected features are the focus of clinical diagnosis reports, indicating that the feature selection algorithm is effective.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Enfermedad de Alzheimer
/
Disfunción Cognitiva
Tipo de estudio:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Ther Innov Regul Sci
Año:
2022
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
CH
/
SUIZA
/
SUÍÇA
/
SWITZERLAND