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A ResNet mini architecture for brain age prediction.

Authors :
Zhang, Xuan
Duan, Si-Yuan
Wang, Si-Qi
Chen, Yao-Wen
Lai, Shi-Xin
Zou, Ji-Sheng
Cheng, Yan
Guan, Ji-Tian
Wu, Ren-Hua
Zhang, Xiao-Lei
Source :
Scientific Reports. 5/16/2024, Vol. 14 Issue 1, p1-8. 8p.
Publication Year :
2024

Abstract

The brain presents age-related structural and functional changes in the human life, with different extends between subjects and groups. Brain age prediction can be used to evaluate the development and aging of human brain, as well as providing valuable information for neurodevelopment and disease diagnosis. Many contributions have been made for this purpose, resorting to different machine learning methods. To solve this task and reduce memory resource consumption, we develop a mini architecture of only 10 layers by modifying the deep residual neural network (ResNet), named ResNet mini architecture. To support the ResNet mini architecture in brain age prediction, the brain age dataset (OpenNeuro #ds000228) that consists of 155 study participants (three classes) and the Alzheimer MRI preprocessed dataset that consists of 6400 images (four classes) are employed. We compared the performance of the ResNet mini architecture with other popular networks using the two considered datasets. Experimental results show that the proposed architecture exhibits generality and robustness with high accuracy and less parameter number. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
*AGE

Details

Language :
English
ISSN :
20452322
Volume :
14
Issue :
1
Database :
Academic Search Index
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
177309551
Full Text :
https://doi.org/10.1038/s41598-024-61915-5