A Reparametrized CNN Model to Distinguish Alzheimer's Disease Applying Multiple Morphological Metrics and Deep Semantic Features From Structural MRI

Zhenpeng Chen; Xiao Mo; Rong Chen; Pujie Feng; Haiyun Li

doi:10.3389/fnagi.2022.856391

A Reparametrized CNN Model to Distinguish Alzheimer's Disease Applying Multiple Morphological Metrics and Deep Semantic Features From Structural MRI

Front Aging Neurosci. 2022 May 26:14:856391. doi: 10.3389/fnagi.2022.856391. eCollection 2022.

Authors

Zhenpeng Chen¹, Xiao Mo¹, Rong Chen¹, Pujie Feng¹, Haiyun Li¹

Affiliation

¹ Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, China.

Abstract

It is of potential clinical value to improve the accuracy of Alzheimer's disease (AD) recognition using structural MRI. We proposed a reparametrized convolutional neural network (Re-CNN) to discriminate AD from NC by applying morphological metrics and deep semantic features. The deep semantic features were extracted through Re-CNN on structural MRI. Considering the high redundancy in deep semantic features, we constrained the similarity of the features and retained the most distinguishing features utilizing the reparametrized module. The Re-CNN model was trained in an end-to-end manner on structural MRI from the ADNI dataset and tested on structural MRI from the AIBL dataset. Our proposed model achieves better performance over some existing structural MRI-based AD recognition models. The experimental results show that morphological metrics along with the constrained deep semantic features can relatively improve AD recognition performance. Our code is available at: https://github.com/czp19940707/Re-CNN.

Keywords: Alzheimer's disease; deep semantic features; multiple morphological metrics; reparametrized CNN; structural MRI.