Application of deep learning to mild cognitive impairment conversion and classification
10.7687/j.issn1003-8868.2017.09.105
- VernacularTitle:深度学习在轻度认知障碍转化与分类中的应用分析
- Author:
wen Bai ZHANG
1
;
Lan LIN
;
cai Shui WU
Author Information
1. 北京工业大学生命科学与生物工程学院
- Keywords:
deep learning;
mild cognitive impairment;
Alzheimer's disease;
structural magnetic resonance imaging;
ADNI database
- From:
Chinese Medical Equipment Journal
2017;38(9):105-111
- CountryChina
- Language:Chinese
-
Abstract:
Mild cognitive impairment (MCI) is a prodromal stage of dementia.Predicting MCI's conversion to Alzheimer's disease (AD) plays critical roles in preventing the progression of AD.Alzheimer's disease neuroimaging initiative (ADNI) was introduced briefly,which was a widely used neuroimaging database for the study on AD related diseases,and the application of machine learning algorithm was reviewed in MCI classification.Deep learning network,which transforms the original data into a higher level and more abstract expression,has shown great promise in MCI conversion and classification.Two main kinds of deep learning approaches were described,including supervised learning and unsupervised learning,and their new application was discussed in MCI conversion and classification based on structural magnetic resonance imaging (sMRI).Finally,the current limitations and future trends of deep learning in this area were explored.
