Early prognosis of Alzheimer's disease based on convolutional neural networks and ensemble learning.
10.7507/1001-5515.201809040
- Author:
An ZENG
1
,
2
;
Longfei JIA
3
;
Dan PAN
4
,
5
;
Xiaowei SONG
6
Author Information
1. School of Computers, Guangdong University of Technology, Guangzhou 510006, P.R.China
2. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510006, P.R.China.
3. School of Computers, Guangdong University of Technology, Guangzhou 510006, P.R.China.
4. Modern Educational Technology Center, Guangdong Construction Polytechnic, Guangzhou 510440, P.R.China
5. Guangzhou Dazhi Networks Technology Co. Ltd., Guangzhou 510000, P.R.China.2656351065@qq.com.
6. ImageTech Lab, Simon Fraser University, Vancouver V6B 5K3, Canada.
- Publication Type:Journal Article
- Keywords:
Alzheimer's disease;
convolutional neural networks;
ensemble learning;
magnetic resonance imaging;
mild cognitive impairment
- MeSH:
Alzheimer Disease;
diagnosis;
Brain;
diagnostic imaging;
Cognitive Dysfunction;
Deep Learning;
Humans;
Neural Networks (Computer);
Neuroimaging;
Prognosis
- From:
Journal of Biomedical Engineering
2019;36(5):711-719
- CountryChina
- Language:Chinese
-
Abstract:
Alzheimer's disease (AD) is a typical neurodegenerative disease, which is clinically manifested as amnesia, loss of language ability and self-care ability, and so on. So far, the cause of the disease has still been unclear and the course of the disease is irreversible, and there has been no cure for the disease yet. Hence, early prognosis of AD is important for the development of new drugs and measures to slow the progression of the disease. Mild cognitive impairment (MCI) is a state between AD and healthy controls (HC). Studies have shown that patients with MCI are more likely to develop AD than those without MCI. Therefore, accurate screening of MCI patients has become one of the research hotspots of early prognosis of AD. With the rapid development of neuroimaging techniques and deep learning, more and more researchers employ deep learning methods to analyze brain neuroimaging images, such as magnetic resonance imaging (MRI), for early prognosis of AD. Hence, in this paper, a three-dimensional multi-slice classifiers ensemble based on convolutional neural network (CNN) and ensemble learning for early prognosis of AD has been proposed. Compared with the CNN classification model based on a single slice, the proposed classifiers ensemble based on multiple two-dimensional slices from three dimensions could use more effective information contained in MRI to improve classification accuracy and stability in a parallel computing mode.
