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An efficient 3D dee...
An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images
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- Bäckström, Karl, 1994 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Nazari, Mahmood (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- 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.
- Related links:
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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
Publication and Content Type
- kon (subject category)
- ref (subject category)
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