Dynamic Image for 3D MRI Image Alzheimer's Disease Classification.
Comput Vis ECCV
; 12535: 355-364, 2020 Aug.
Article
en En
| MEDLINE
| ID: mdl-37283785
ABSTRACT
We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. We show our proposed CNN model achieves 9.5% better Alzheimer's disease classification accuracy than the baseline 3D models. We also show that our method allows for efficient training, requiring only 20% of the training time compared to 3D CNN models. The code is available online https//github.com/UkyVision/alzheimer-project.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
Comput Vis ECCV
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos