Exploring multifractal-based features for mild Alzheimer's disease classification

2015 ◽  
Vol 76 (1) ◽  
pp. 259-269 ◽  
Author(s):  
Huangjing Ni ◽  
Luping Zhou ◽  
Xinbao Ning ◽  
Lei Wang ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Classification

Lightweight deep residual network for alzheimer’s disease classification using sMRI slices

2021 ◽  
pp. 1-9
Author(s):  
Yanteng Zhang ◽  
Qizhi Teng ◽  
Linbo Qing ◽  
Yan Liu ◽  
Xiaohai He
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Computational Cost ◽  
Disease Classification ◽  
Learning Networks ◽  
Feature Maps ◽  
Medical Field ◽  
Widespread Application ◽  
Healthy Control

Alzheimer’s disease (AD) is a degenerative brain disease and the most common cause of dementia. In recent years, with the widespread application of artificial intelligence in the medical field, various deep learning-based methods have been applied for AD detection using sMRI images. Many of these networks achieved AD vs HC (Healthy Control) classification accuracy of up to 90%but with a large number of computational parameters and floating point operations (FLOPs). In this paper, we adopt a novel ghost module, which uses a series of cheap operations of linear transformation to generate more feature maps, embedded into our designed ResNet architecture for task of AD vs HC classification. According to experiments on the OASIS dataset, our lightweight network achieves an optimistic accuracy of 97.92%and its total parameters are dozens of times smaller than state-of-the-art deep learning networks. Our proposed AD classification network achieves better performance while the computational cost is reduced significantly.

Alzheimer’s Disease Classification Using Genetic Data

2021 ◽  
Author(s):  
Subash Khanal ◽  
Jin Chen ◽  
Nathan Jacobs ◽  
Ai-Ling Lin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genetic Data ◽  
Disease Classification
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