BACKGROUND The detection of Alzheimer's Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classification of MCI-converts and MCI-non-converts has not been explored profusely and the maximum classification accuracy reported is rather low. Thus, this paper proposes a Machine Learning approach for classifying patients of MCI into two groups one who converted to AD and the others who are not diagnosed with any signs of AD. The proposed algorithm is also used to distinguish MCI patients from controls (CN). This work uses the Structural Magnetic Resonance Imaging data. METHODS This work proposes a 3-D variant of Local Binary Pattern (LBP), called LBP-20 for extracting features. The method has been compared with 3D-Discrete Wavelet Transform (3D-DWT). Subsequently, a combination of 3D-DWT and LBP-20 has been used for extracting features. The relevant features are selected using the Fisher Discriminant Ratio (FDR) and finally the classification has been carried out using the Support Vector Machine. RESULTS The combination of 3D-DWT with LBP-20 results in a maximum accuracy of 88.77. Similarly, the proposed combination of methods is also applied to distinguish MCI from CN. The proposed method results in the classification accuracy of 90.31 in this data. CONCLUSION The proposed combination is able to extract relevant distribution of microstructures from each component, obtained with the use of DWT and thereby improving the classification accuracy. Moreover, the number of features used for classification is significantly less as compared to those obtained by 3D-DWT. The performance of the proposed method is measured in terms of accuracy, specificity and sensitivity and is found superior in comparison to the existing methods. Thus, the proposed method may contribute to effective diagnosis of MCI and may prove advantageous in clinical settings.

中文翻译：

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3-D 离散小波变换和 3-D 局部二值模式的组合用于轻度认知障碍的分类。


背景技术阿尔茨海默病（AD）形成阶段的检测，尤其是轻度认知障碍（MCI），有可能帮助临床医生了解这种疾病。文献综述表明，MCI 转换者和 MCI 未转换者的分类尚未得到广泛探索，并且报告的最大分类精度相当低。因此，本文提出了一种机器学习方法，用于将 MCI 患者分为两组，一组转变为 AD，另一组未诊断出任何 AD 迹象。所提出的算法还用于区分 MCI 患者与对照 (CN)。这项工作使用结构磁共振成像数据。方法 这项工作提出了一种局部二值模式 (LBP) 的 3D 变体，称为 LBP-20，用于提取特征。该方法与 3D 离散小波变换 (3D-DWT) 进行了比较。随后，3D-DWT 和 LBP-20 的组合被用于提取特征。使用费舍尔判别比（FDR）选择相关特征，最后使用支持向量机进行分类。结果 3D-DWT 与 LBP-20 的组合产生的最大精度为 88.77。类似地，所提出的方法组合也适用于区分 MCI 和 CN。所提出的方法在该数据中的分类精度为 90.31。结论所提出的组合能够从使用 DWT 获得的每个成分中提取微观结构的相关分布，从而提高分类精度。此外，与 3D-DWT 获得的特征相比，用于分类的特征数量明显较少。 该方法的性能在准确性、特异性和灵敏度方面进行了衡量，并且与现有方法相比优于现有方法。因此，所提出的方法可能有助于有效诊断 MCI，并可能在临床环境中证明是有利的。