Alzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD. The main motive of this paper is to improve the classification and prediction accuracy of AD. In this paper, a novel approach is developed to classify MCI, normal control (NC), and AD using structural magnetic resonance imaging (sMRI) from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset (50 AD, 50 NC, 50 MCI subjects). FreeSurfer is used to process these MRI data and obtain cortical features such as volume, surface area, thickness, white matter (WM), and intrinsic curvature of the brain regions. These features are modified by normalizing each cortical region's features using the absolute maximum value of that region's features from all subjects in each group of MCI, NC, and AD independently. A total of 420 features are obtained. To address the curse of dimensionality, the obtained features are reduced to 30 features using a sequential feature selection technique. Three classifiers, namely the twin support vector machine (TSVM), least squares TSVM (LSTSVM), and robust energy‐based least squares TSVM (RELS‐TSVM), are used to evaluate the classification accuracy from the obtained features. Five‐fold and 10‐fold cross‐validation are used to validate the proposed method. Experimental results show an accuracy of 100% for the studied database. The proposed approach is innovative due to its higher classification accuracy compared to methods in the existing literature.

中文翻译：

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使用磁共振成像从正常对照中分类阿尔茨海默氏病的新方法

阿尔茨海默氏病（AD）是痴呆症最普遍的形式。尽管由于对AD的病因和治疗方法的了解不足，可以正确，迅速地诊断出患有AD的人，但如果轻度认知障碍（MCI）的症状处于早期阶段，AD的管理就更容易了。近年来，计算机辅助诊断已广泛用于AD的诊断。本文的主要目的是提高AD的分类和预测精度。在本文中，开发了一种新颖的方法，使用来自阿尔茨海默氏病神经影像计划（ADNI）数据集的结构磁共振成像（sMRI）对MCI，正常对照（NC）和AD进行分类（50个AD，50个NC，50个MCI受试者） 。FreeSurfer用于处理这些MRI数据并获得皮质特征，例如体积，表面积，厚度，白质（WM）和大脑区域的固有曲率。通过使用来自MCI，NC和AD每组中所有对象的该区域的特征的绝对最大值，对每个皮质区域的特征进行归一化，可以修改这些特征。总共获得420个特征。为了解决维数的诅咒，使用顺序特征选择技术将获得的特征缩减为30个特征。三种分类器，即双支持向量机（TSVM），最小二乘TSVM（LSTSVM）和鲁棒的基于能量的最小二乘TSVM（RELS-TSVM），用于从获得的特征中评估分类准确性。五倍和十倍交叉验证用于验证所提出的方法。实验结果表明，所研究数据库的准确性为100％。