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An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images

Bäckström, Karl, 1994 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Nazari, Mahmood (author)
Chalmers tekniska högskola,Chalmers University of Technology
Gu, Irene Yu-Hua, 1953 (author)
Chalmers tekniska högskola,Chalmers University of Technology
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Jakola, Asgeir Store (author)
Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi,Institute of Neuroscience and Physiology
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 (creator_code:org_t)
IEEE, 2018
2018
English.
  • Conference paper (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Medical Image Processing (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Neurosciences (hsv//eng)

Keyword

MR imaging.
computer-aided diagnosis
automatic feature learning
deep learning
3D deep convolutional networks
Alzheimer’s disease detection
Alzheimer's disease detection
3D deep convolutional networks
automatic feature learning
deep learning
computer-aided diagnosis
MR imaging

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