Towards Effective Classification of aMCI Based on Resting-State Multiscale Brain Features and Machine Learning Approaches
Smart healthcare has undergone new opportunities and challenges with the arrival of the Industry 4.0 era. The intelligent imaging diagnosis system is a staple part of smart healthcare, helping doctors make clinical decisions. Nevertheless, intelligent diagnosis analysis is still confronted with the...
Main Authors: | Chunting Cai, Jiqiang Yan, Yu Zhou, Wuyang Zheng, Chenhui Yang, Zhemin Zhang, Bokui Chen, Dan Hong |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi-Wiley
2021-01-01
|
Series: | Wireless Communications and Mobile Computing |
Online Access: | http://dx.doi.org/10.1155/2021/9975237 |
Similar Items
-
Selective Changes of Resting-State Brain Oscillations in aMCI: An fMRI Study Using ALFF
by: Zhilian Zhao, et al.
Published: (2014-01-01) -
Aberrant hippocampal subregion networks associated with the classifications of aMCI subjects: a longitudinal resting-state study.
by: Feng Bai, et al.
Published: (2011-01-01) -
Electrophysiological Studies of Cognitive Reappraisal Success and Failure in aMCI
by: Shasha Xiao, et al.
Published: (2021-06-01) -
The Uncinate Fasciculus as a Predictor of Conversion from aMCI to Alzheimer Disease
by: Hiyoshi-Taniguchi, Kazuko
Published: (2015) -
A Longitudinal Study of Episodic and Semantic Autobiographical Memory in aMCI and Alzheimer’s Disease Patients
by: Juan C. Meléndez, et al.
Published: (2021-06-01)