An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images
Paper in proceedings, 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, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Mahmood Nazari

Irene Yu-Hua Gu

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Asgeir Jakola

University of Gothenburg


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

Driving Forces

Sustainable development

Areas of Advance

Life Science Engineering (2010-2018)

Subject Categories

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing



More information

Latest update