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首页> 外文期刊>BMC Medical Informatics and Decision Making >Effective diagnosis of Alzheimer’s disease by means of large margin-based methodology
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Effective diagnosis of Alzheimer’s disease by means of large margin-based methodology

机译：通过基于大余量的方法有效诊断阿尔茨海默氏病

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Background Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer’s Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. Methods It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. Results Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. Conclusions All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).
机译：背景功能性大脑图像，例如单光子发射计算机断层扫描（SPECT）和正电子发射断层扫描（PET），已被广泛用于指导临床医生进行阿尔茨海默氏病（AD）诊断。但是，评估中涉及的主观因素有利于计算机辅助诊断（CAD）系统的开发。方法提出了一种特征提取技术的新型组合，以改善AD的诊断。首先，通过对3D标准化均方误差（NMSE）特征执行的t检验选择感兴趣区域（ROI），该特征仅限于位于预定义的大脑激活蒙版内。为了解决样本量小的问题，通过以下方法进一步减小了特征空间的尺寸：使用矩形矩阵（LMNN-RECT），主成分分析（PCA）或偏最小二乘（PLS）的大余量最近邻居（后者还使用LMNN转换进行了分析）。关于分类器，比较了使用欧几里得，马哈拉诺比斯和基于能源的指标的内核支持向量机（SVM）和LMNN。结果为了评估所提出的基于LMNN的特征提取算法及其优势，进行了一些实验：i）PLS或PCA约简数据的线性变换，ii）特征约简技术和iii）分类器（使用欧几里得，马哈拉诺比斯或基于能源的方法）。当进行k倍交叉验证时，该系统的准确性，敏感性和特异性值分别为92.78％，91.07％和95.12％（对于SPECT）和90.67％，88％和93.33％（对于PET），通过k倍交叉验证进行评估。 NMSE-PLS-LMNN特征提取方法与SVM分类器结合使用，因此胜过最近报道的基线方法。结论事实证明，所有提出的方法都是对所提出问题的有效解决方案。进步之一是LMNN算法的鲁棒性，它不仅在类之间提供了更高的分离率，而且（与NMSE和PLS结合使用）使这种速率变化更加稳定。此外，它们的泛化能力是另一项进步，因为对两种图像模式（SPECT和PET）进行了多次实验。

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