Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities
Background: Alzheimer's Disease (AD) is a neurodegenerative disease characterized by progressive loss of memory and general decline in cognitive functions. Multi-modal imaging such as structural MRI and DTI provide useful information for the classification of patients on the basis of brain biom...
Main Authors: | Karim Aderghal, Karim Afdel, Jenny Benois-Pineau, Gwénaëlle Catheline |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2020-12-01
|
Series: | Heliyon |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844020324956 |
Similar Items
-
Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network
by: Yechong Huang, et al.
Published: (2019-05-01) -
Localization and Identification of Brain Microstructural Abnormalities in Paediatric Concussion
by: David Stillo, et al.
Published: (2021-05-01) -
Comparison of Five Conductivity Tensor Models and Image Reconstruction Methods Using MRI
by: Nitish Katoch, et al.
Published: (2021-09-01) -
Early functional connectivity deficits and progressive microstructural alterations in the TgF344-AD rat model of Alzheimer’s Disease: A longitudinal MRI study
by: Cynthia Anckaerts, et al.
Published: (2019-04-01) -
Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer’s disease
by: Alice Motovylyak, et al.
Published: (2022-01-01)