摘要
阿尔茨海默病(AD)是一种中枢神经系统的慢性神经退行性疾病,它不能治疗并能导致死亡。其中一个最常见的阿尔茨海默病诊断工具是磁共振成像(MRI),因为它可视化脑解剖结构的能力。有多种分类方法的自动诊断阿尔茨海默病,如支持向量机,遗传算法,贝叶斯分类器,神经网络,随机森林等,但他们没有提供阿尔茨海默病阶段的强大信息,他们可以只揭示疾病的存在。 在本文中,提出了一种新的方法,即,使用模糊推理系统的磁共振成像图像分类。116个解剖区域(ROIs)的两组统计量(均值和标准差)作为系统分类的输入特征。t检验的特征选择方法是用来确定最明显的解剖区域。为了评估这个系统,磁共振成像图像组成的一个数据库有818课题(229正常,401轻度认知障碍和188阿尔茨海默病),它们从阿尔茨海默病神经影像学倡议(ADNI)分析收集。接受者操作特征(ROC)曲线下面积(AUC),所提出的模糊推理系统由统计输入特征作为评价准则与交叉验证。该系统在训练集产生正常与阿尔茨海默病分类的曲线下面积为0.99和在测试集产生0.8622±0.0033的成果。
关键词: 阿尔茨海默病,分类,诊断,模糊逻辑,模糊推理系统,轻度认知功能障碍,磁共振成像。
Current Alzheimer Research
Title:Fuzzy Computer-Aided Alzheimer’s Disease Diagnosis Based on MRI Data
Volume: 13 Issue: 5
Author(s): Igor Krashenyi, Javier Ramírez, Anton Popov, Juan Manuel Górriz and the Alzheimer’s Disease Neuroimaging Initiative
Affiliation:
关键词: 阿尔茨海默病,分类,诊断,模糊逻辑,模糊推理系统,轻度认知功能障碍,磁共振成像。
摘要: Alzheimer’s disease (AD) is a chronic neurodegenerative disease of the central nervous system that has no cure and leads to death. One of the most prevalent tools for AD diagnosis is magnetic resonance imaging (MRI), because of its capability to visualize brain anatomical structures. There is a variety of classification methods for automatic diagnosis of AD, such as support vector machines, genetic algorithms, Bayes classifiers, neural networks, random forests, etc., but none of them provides robust information about the stage of the AD, they can just reveal the presence of disease.
In this paper, a new approach for classification of MRI images using a fuzzy inference system is proposed. Two statistical moments (mean and standard deviation) of 116 anatomical regions of interests (ROIs) are used as input features for the classification system. A t-test feature selection method is used to identify the most discriminative ROIs. In order to evaluate the proposed system, MRI images from a database consisting of 818 subjects (229 normal, 401 mild cognitive impairment and 188 AD subjects) collected from the Alzheimer’s disease neuroimaging initiative (ADNI) is analyzed. The receiver operating characteristics (ROC) curve and the area under the curve (AUC) of the proposed fuzzy inference system fed by statistical input features are employed as the evaluation criteria with k-fold cross validation. The proposed system yields promising results in normal vs. AD classification with AUC of 0.99 on the training set and 0.8622±0.0033 on the testing set.
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Igor Krashenyi, Javier Ramírez, Anton Popov, Juan Manuel Górriz and the Alzheimer’s Disease Neuroimaging Initiative , Fuzzy Computer-Aided Alzheimer’s Disease Diagnosis Based on MRI Data, Current Alzheimer Research 2016; 13 (5) . https://dx.doi.org/10.2174/1567205013666160314145008
DOI https://dx.doi.org/10.2174/1567205013666160314145008 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |

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