Research of Low-Rank Representation and Discriminant Correlation Analysis for Alzheimer's Disease Diagnosis.

Zhigang Li,Aimei Dong,Jing Zhou

doi:10.1155/2020/5294840

Zhigang Li, Aimei Dong + Show 1 more

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https://doi.org/10.1155/2020/5294840

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Abstract

As population aging is becoming more common worldwide, applying artificial intelligence into the diagnosis of Alzheimer's disease (AD) is critical to improve the diagnostic level in recent years. In early diagnosis of AD, the fusion of complementary information contained in multimodality data (e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF)) has obtained enormous achievement. Detecting Alzheimer's disease using multimodality data has two difficulties: (1) there exists noise information in multimodal data; (2) how to establish an effective mathematical model of the relationship between multimodal data? To this end, we proposed a method named LDF which is based on the combination of low-rank representation and discriminant correlation analysis (DCA) to fuse multimodal datasets. Specifically, the low-rank representation method is used to extract the latent features of the submodal data, so the noise information in the submodal data is removed. Then, discriminant correlation analysis is used to fuse the submodal data, so the complementary information can be fully utilized. The experimental results indicate the effectiveness of this method.

Highlights

Alzheimer’s disease (AD) is a type of neurodegenerative disease, which is caused by many factors
In view of the above problems, we propose a feature fusion method founded on the combination of low-rank representation and discriminant correlation analysis. e proposed method has three advantages: (1) noise reduction and subspace feature learning of the original data reduce the noise value and redundancy of the data; (2) maximize the pairwise correlation between the submodal and the submodal features and effectively simulate the relationship between the modes; (3) replace the original features with fusion features to avoid noise information to the greatest extent
Due to the lack of data, the KNN algorithm is used to complete the data first; secondly, low-rank representation is used to extract the potential features of the data and denoise the multimodal data; thirdly, discriminant correlation analysis is used to model the submodal data and get the fusion matrix; the fusion results are input into the support vector machine classifier for classification, see Figure 1 for detail

Summary

Introduction

Alzheimer’s disease (AD) is a type of neurodegenerative disease, which is caused by many factors. Detection is the key to the treatment of Alzheimer’s disease, but it is very difficult to diagnose Alzheimer’s disease due to the diversity of its causes. Using medical imaging technology [3,4,5,6] to assist clinical staff in diagnosis is the primary method to detect Alzheimer’s disease. Because of the diversity of the causes of Alzheimer’s disease and the phenomenon of brain atrophy, it is difficult to capture all the immunological information contained in the medical image only by the naked eye. In the past decade, people began to identify Alzheimer’s disease by machine learning.

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