An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images
Paper i proceeding, 2018

Automatic extraction of features from MRI brain scans and diagnosis of Alzheimer’s Disease (AD) remain a challenging task. In this paper, we propose an efficient and simple three dimensional convolutional network (3D ConvNet) architecture that is able to achieve high performance for detection of AD on a relatively large dataset. The proposed 3D ConvNet consists of five convolutional layers for feature extraction, followed by three fully-connected layers for AD/NC classification. The main contributions of the paper include: (a) propose a novel and effective 3D ConvNet architecture; (b) study the impact of hyper-parameter selection on the performance of AD classification; (c) study the impact of pre-processing; (d) study the impact of data partitioning; (e) study the impact of dataset size. Experiments conducted on an ADNI dataset containing 340 subjects and 1198 MRI brain scans have resulted good performance (with the test accuracy of 98.74%, 100% AD detection rate and 2,4% false alarm). Comparisons with 7 existing state-of-the-art methods have provided strong support to the robustness of the proposed method.

MR imaging.

computer-aided diagnosis

automatic feature learning

deep learning

3D deep convolutional networks

Alzheimer’s disease detection


Karl Bäckström

Chalmers, Data- och informationsteknik, Nätverk och system

Mahmood Nazari

Irene Yu-Hua Gu

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Asgeir Jakola

Göteborgs universitet


IEEE int'l symposium on biomedical imaging (ISBI'18)
washington DC, USA, ,


Hållbar utveckling


Livsvetenskaper och teknik (2010-2018)


Datorseende och robotik (autonoma system)

Medicinsk bildbehandling



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