- Title
- Automatic Alzheimer's disease recognition from MRI data using deep learning method
- Creator
- Luo, Suhuai; Li, Xuechen; Li, Jiaming
- Relation
- Journal of Applied Mathematics and Physics Vol. 5, Issue 9, p. 1892-1898
- Publisher Link
- http://dx.doi.org/10.4236/jamp.2017.59159
- Publisher
- Scientific Research Publishing
- Resource Type
- journal article
- Date
- 2017
- Description
- Alzheimer’s Disease (AD), the most common form of dementia, is an incurable neurological condition that results in a progressive mental deterioration. Although definitive diagnosis of AD is difficult, in practice, AD diagnosis is largely based on clinical history and neuropsychological data including magnetic resource imaging (MRI). Increasing research has been reported on applying machine learning to AD recognition in recent years. This paper presents our latest contribution to the advance. It describes an automatic AD recognition algorithm that is based on deep learning on 3D brain MRI. The algorithm uses a convolutional neural network (CNN) to fulfil AD recognition. It is unique in that the three dimensional topology of brain is considered as a whole in AD recognition, resulting in an accurate recognition. The CNN used in this study consists of three consecutive groups of processing layers, two fully connected layers and a classification layer. In the structure, every one of the three groups is made up of three layers, including a convolutional layer, a pooling layer and a normalization layer. The algorithm was trained and tested using the MRI data from Alzheimer’s Disease Neuroimaging Initiative. The data used include the MRI scanning of about 47 AD patients and 34 normal controls. The experiment had shown that the proposed algorithm delivered a high AD recognition accuracy with a sensitivity of 1and a specificity of 0.93.
- Subject
- Alzheimer’s disease; AD; recognition; magnetic resource imaging; MRI; deep Learning; convolutional neural network; CNN
- Identifier
- http://hdl.handle.net/1959.13/1350583
- Identifier
- uon:30576
- Identifier
- ISSN:2327-4352
- Rights
- This is an open-access article distributed under the terms of the Creative Commons Attribution License
- Language
- eng
- Full Text
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