Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.3390/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
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EISSN | 2076-3425 |
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). |
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