Early diagnosis of Alzheimer's disease based on three-dimensional convolutional neural networks ensemble model combined with genetic algorithm.
10.7507/1001-5515.201911046
- Author:
Dan PAN
1
;
Chao ZOU
2
;
Huabin RONG
2
;
An ZENG
2
Author Information
1. School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou 510665, P.R.China.
2. Faculty of Computer, Guangdong University of Technology, Guangzhou 510006, P.R.China.
- Publication Type:Journal Article
- Keywords:
Alzheimer's disease;
classification;
convolutional neural network;
genetic algorithm;
region of interest
- MeSH:
Alzheimer Disease/diagnosis*;
Brain/diagnostic imaging*;
Cognitive Dysfunction/diagnosis*;
Early Diagnosis;
Humans;
Magnetic Resonance Imaging;
Neural Networks, Computer;
Neurodegenerative Diseases
- From:
Journal of Biomedical Engineering
2021;38(1):47-55
- CountryChina
- Language:Chinese
-
Abstract:
The pathogenesis of Alzheimer's disease (AD), a common neurodegenerative disease, is still unknown. It is difficult to determine the atrophy areas, especially for patients with mild cognitive impairment (MCI) at different stages of AD, which results in a low diagnostic rate. Therefore, an early diagnosis model of AD based on 3-dimensional convolutional neural network (3DCNN) and genetic algorithm (GA) was proposed. Firstly, the 3DCNN was used to train a base classifier for each region of interest (ROI). And then, the optimal combination of the base classifiers was determined with the GA. Finally, the ensemble consisting of the chosen base classifiers was employed to make a diagnosis for a patient and the brain regions with significant classification capability were decided. The experimental results showed that the classification accuracy was 88.6% for AD
