Predicting clinical scores for Alzheimer’s disease based on joint and deep learning

作者：

Highlights：

• We design a joint and deep learning framework to predict the clinical scores of AD.

• We use the group LASSO and correntropy for dimension reduction via feature selection.

• We explore the multi-layer independently recurrent neural network regression.

• We predict the clinical score by learning relationship between MRI and clinical score.

摘要

•We design a joint and deep learning framework to predict the clinical scores of AD.•We use the group LASSO and correntropy for dimension reduction via feature selection.•We explore the multi-layer independently recurrent neural network regression.•We predict the clinical score by learning relationship between MRI and clinical score.

论文关键词：Alzheimer's disease,Feature selection,Deep learning,Independently recurrent neural network,Score prediction

论文评审过程：Received 29 January 2021, Revised 23 June 2021, Accepted 21 September 2021, Available online 27 September 2021, Version of Record 9 October 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115966