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.
automatic feature learning
3D deep convolutional networks
Alzheimer’s disease detection