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An efficient classification approach for detection of Alzheimer's disease from biomedical imaging modalities

机译：一种高效的分类方法，用于检测生物医学成像方式的阿尔茨海默病

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The complex patterns of the neuroimaging data are analyzed successfully with bio-medical imaging applications. The patients with/without AD can be discriminated effectively through several biomedical imaging modalities such as, sMRI, fMRI, PET and so on. In this paper, brain images from structural MRI (sMRI) are used for better categorization of 3 subjects namely, NC (Normal Control), MCI (Mild Cognitive Impairment) and AD (Alzheimer's disease). Moreover, ambiguous training data employed for the discrimination of subjects may mislead the classifier to take incorrect decisions and in turn degrades the classification performance. In order to recover these hurdles, we propose an automated reliable system for the detection of AD affected patients accurately from the brain images of sMRI. The proposed system is a multi-stage system comprising four key phases namely, i) pre-processing, ii) feature extraction, iii) feature selection and iv) detection phase. In the initial phase, ROI regions related to Hippocampus (HC) and Posterior Cingulate Cortex (PCC) from the brain images are extracted using Automated Anatomical Labeling (AAL) method. In the feature extraction stage, important texture and shape features are extracted from HC and PCC involved in three brain planes. Nearly, 19 highly relevant AD related features are selected through a multiple-criterion feature selection method. It should be noted that, the class labels when explored manually consumes more time and turns to be an expensive process. Therefore, it is essential to construct an automatic method to identify irrelevant samples in the training data to enhance the decision-making process. With this in mind, at the detection phase, a novel classification technique is proposed by combining the Kernel fuzzy c-means clustering (i.e. unsupervised learning technique) and Back-propagation artificial neural network (i.e. supervised learning technique) to categorize NC, MCI and AD from the brain images of sMRI. This proposed KFCM based BPANN algorithm can improve the classification performance by removing the suspicious training samples. The proposed frameworks efficiency is evaluated with the ADNI subset and then to the Bordeaux-3 city dataset. The experimental validation of our proposed approach attains an accuracy of 97.63%, 95.4%, 96.4% for the most challenging classification tasks AD vs NC, MCI vs NC and AD vs MCI, respectively.
机译：用生物医学成像应用成功分析了神经影像数据的复杂模式。患有/不带广告的患者可以通过若干生物医学成像模态进行有效地进行歧视，例如SMRI，FMRI，PET等。本文中，来自结构MRI（SMRI）的脑图像用于更好地分类3个受试者，即NC（正常对照），MCI（轻度认知障碍）和广告（Alzheimer疾病）。此外，用于歧视受试者的模糊培训数据可能会误导分类器，以采取不正确的决策，并反过来降低分类性能。为了恢复这些障碍，我们提出了一种自动可靠的系统，用于从SMRI的脑图像中准确地检测受影响的患者。所提出的系统是一种多级系统，包括四个密钥相，即i）预处理，ii）特征提取，III）特征选择和IV）检测阶段。在初始阶段中，使用自动解剖标记（AAL）方法提取与脑图像中的海马（HC）和后铰接皮质（PCC）相关的ROI区域。在特征提取阶段，从HC和PCC中提取了重要的纹理和形状特征，涉及三个脑平面。近，通过多标准特征选择方法选择19个高度相关的广告相关特征。应该注意的是，探索手动消耗的课程标签并变成昂贵的过程。因此，必须构建一种自动方法来识别训练数据中的无关样本，以增强决策过程。考虑到这一点，在检测阶段，通过组合内核模糊C型聚类（即无监督的学习技术）和反向传播人工神经网络（即监督学习技术）来提出一种新的分类技术来分类NC，MCI和来自SMRI的脑图像的广告。这一提出的基于KFCM的BPANN算法可以通过去除可疑培训样本来改善分类性能。建议的框架效率与ADNI子集进行评估，然后评估Bordeaux-3 City数据集。我们所提出的方法的实验验证分别获得了97.63％，95.4％，95.4％，95.4％，分别为最具挑战性的分类任务，MCI与NC和AD VS MCI分别为97.63％，95.4％，96.4％。

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