Arid
DOI10.3390/brainsci7080109
Alzheimer’s Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning
Khajehnejad, Moein; Saatlou, Forough Habibollahi; Mohammadzade, Hoda
通讯作者Mohammadzade, Hoda
来源期刊BRAIN SCIENCES
EISSN2076-3425
出版年2017
卷号7期号:8
英文摘要

Alzheimer’s disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer’s disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient approach for accurate prediction of the condition of the brain through the classification of magnetic resonance imaging (MRI) images is greatly beneficial and yet very challenging. In this paper, a novel approach is proposed for the diagnosis of very early stages of AD through an efficient classification of brain MRI images, which uses label propagation in a manifold-based semi-supervised learning framework. We first apply voxel morphometry analysis to extract some of the most critical AD-related features of brain images from the original MRI volumes and also gray matter (GM) segmentation volumes. The features must capture the most discriminative properties that vary between a healthy and Alzheimer-affected brain. Next, we perform a principal component analysis (PCA)-based dimension reduction on the extracted features for faster yet sufficiently accurate analysis. To make the best use of the captured features, we present a hybrid manifold learning framework which embeds the feature vectors in a subspace. Next, using a small set of labeled training data, we apply a label propagation method in the created manifold space to predict the labels of the remaining images and classify them in the two groups of mild Alzheimer’s and normal condition (MCI/NC). The accuracy of the classification using the proposed method is 93.86% for the Open Access Series of Imaging Studies (OASIS) database of MRI brain images, providing, compared to the best existing methods, a 3% lower error rate.


英文关键词Alzheimer’s disease early diagnosis semi-supervised manifold learning label propagation voxel-based morphometry medical image analysis image classification
类型Article
语种英语
国家Iran
收录类别SCI-E
WOS记录号WOS:000408881300017
WOS关键词COMPUTER-AIDED DIAGNOSIS ; VOXEL-BASED MORPHOMETRY ; CLASSIFICATION ; MRI ; SEGMENTATION
WOS类目Neurosciences
WOS研究方向Neurosciences & Neurology
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/197957
作者单位Sharif Univ Technol, Dept Elect Engn, Azadi Ave, Tehran 1458889694, Iran
推荐引用方式
GB/T 7714
Khajehnejad, Moein,Saatlou, Forough Habibollahi,Mohammadzade, Hoda. Alzheimer’s Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning[J],2017,7(8).
APA Khajehnejad, Moein,Saatlou, Forough Habibollahi,&Mohammadzade, Hoda.(2017).Alzheimer’s Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning.BRAIN SCIENCES,7(8).
MLA Khajehnejad, Moein,et al."Alzheimer’s Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning".BRAIN SCIENCES 7.8(2017).
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Khajehnejad, Moein]的文章
[Saatlou, Forough Habibollahi]的文章
[Mohammadzade, Hoda]的文章
百度学术
百度学术中相似的文章
[Khajehnejad, Moein]的文章
[Saatlou, Forough Habibollahi]的文章
[Mohammadzade, Hoda]的文章
必应学术
必应学术中相似的文章
[Khajehnejad, Moein]的文章
[Saatlou, Forough Habibollahi]的文章
[Mohammadzade, Hoda]的文章
相关权益政策
暂无数据
收藏/分享

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。