干旱区生态环境知识资源中心(Arid): Regression and Classification of Alzheimer's Disease Diagnosis Using NMF-TDNet Features From 3D Brain MR Image

Arid

DOI	10.1109/JBHI.2021.3113668
	Regression and Classification of Alzheimer's Disease Diagnosis Using NMF-TDNet Features From 3D Brain MR Image
	Lao, Huan; Zhang, Xuejun
通讯作者	Zhang, XJ
来源期刊	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
ISSN	2168-2194
EISSN	2168-2208
出版年	2022
卷号	26 期号:3 页码:1103-1115
英文摘要	With the development of deep learning and medical imaging technology, many researchers use convolutional neural network(CNN) to obtain deep-level features of medical image in order to better classify Alzheimer's disease (AD) and predict clinical scores. The principal component analysis network (PCANet) is a lightweight deep-learning network that mainly uses principal component analysis (PCA) to generate multilevel filter banks for the centralized learning of samples and then performs binarization and generates blockwise histograms to obtain image features. However, the dimensions of the extracted PCANet features reaching tens of thousands or even hundreds of thousands, and the formation of the multilevel filter banks is sample data dependent, reducing the flexibility of PCANet. In order to solve these problems, in this paper, we propose a data-independent network based on the idea of PCANet, called the nonnegative matrix factorization tensor decomposition network (NMF-TDNet). Specifically, we use nonnegative matrix factorization (NMF) instead of PCA to create multilevel filter banks for sample learning, then uses the learning results to build a higher-order tensor and perform tensor decomposition (TD) to achieve data dimensionality reduction, producing the final image features. Finally, our method use these features as the input of the support vector machine (SVM) for AD classification diagnosis and clinical score prediction. The performance of our method is extensively evaluated on the ADNI-1, ADNI-2 and OASIS datasets. The experimental results show that NMF-TDNet can achieve data dimensionality reduction and the NMF-TDNet features as input achieved superior performance than using PCANet features as input.
英文关键词	Diseases Principal component analysis Tensors Convolution Deep learning Medical diagnostic imaging Matrix decomposition Alzheimer's disease (AD) deep learning PCANet NMF-TDNet regression and classification
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000766665300021
WOS关键词	NONNEGATIVE MATRIX FACTORIZATION ; MILD COGNITIVE IMPAIRMENT ; CONVOLUTIONAL NEURAL-NETWORKS ; PRINCIPAL COMPONENT ANALYSIS ; SUPPORT VECTOR MACHINE ; PARTIAL LEAST-SQUARES ; CORTICAL THICKNESS ; DEMENTIA ; PATTERNS ; PREDICTION
WOS类目	Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS研究方向	Computer Science ; Mathematical & Computational Biology ; Medical Informatics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393070
推荐引用方式 GB/T 7714	Lao, Huan,Zhang, Xuejun. Regression and Classification of Alzheimer's Disease Diagnosis Using NMF-TDNet Features From 3D Brain MR Image[J],2022,26(3):1103-1115.
APA	Lao, Huan,&Zhang, Xuejun.(2022).Regression and Classification of Alzheimer's Disease Diagnosis Using NMF-TDNet Features From 3D Brain MR Image.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,26(3),1103-1115.
MLA	Lao, Huan,et al."Regression and Classification of Alzheimer's Disease Diagnosis Using NMF-TDNet Features From 3D Brain MR Image".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 26.3(2022):1103-1115.