Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer’s disease

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer’s disease

This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI).We used a conventional support vector machine (SVM...

Full description

Bibliographic Details
Main Authors: Esther E. Bron, Stefan Klein, Janne M. Papma, Lize C. Jiskoot, Vikram Venkatraghavan, Jara Linders, Pauline Aalten, Peter Paul De Deyn, Geert Jan Biessels, Jurgen A.H.R. Claassen, Huub A.M. Middelkoop, Marion Smits, Wiro J. Niessen, John C. van Swieten, Wiesje M. van der Flier, Inez H.G.B. Ramakers, Aad van der Lugt
Format: Article
Language:English
Published: Elsevier 2021-01-01
Series:NeuroImage: Clinical
Subjects:
Alzheimer’s disease
Support vector machine
Convolutional Neural Network
External validation
Online Access:http://www.sciencedirect.com/science/article/pii/S221315822100156X

Internet

http://www.sciencedirect.com/science/article/pii/S221315822100156X

Similar Items