Alzheimer's disease is a subject of substantial concern with ample scope for novel discoveries in the field of modern computational medical study for its reputation of being one of the most exorbitant and life-threatening neurodegenerative diseases of the current age. The prime objective of this research is to develop a system that can automatically detect three stages of Alzheimer's disease—Alzheimer's dementia, mild cognitive impairment, and cognitively normal using the traditional machine learning approaches. The dataset collected from Alzheimer's Disease Neuroimaging Initiative containing three types of data as mentioned above with labeled images is used throughout the research. In the proposed method, contrast limited adaptive histogram equalization handles the qualitative visual distortion in advance of feature calculation. Three distinct types of features are identified from structural MR images such as textural, orientational, and spatial features as the gray-level co-occurrence matrix, histogram of oriented gradients, and vector of locally aggregated descriptors. Apart from this, principal component analysis and minimum redundancy maximum relevance operate on the generated feature set for dimensionality reduction and to confer a comparative perspective as well. Experiments conducted upon the availed dataset exhibit that the proposed methodology outperforms other noteworthy existing methods for multiclass detection of Alzheimer's disease achieving accuracy ranging from 94% to 97% with respect to the feature set and models in action. Moreover, a significant outcome is found after applying the findings to a new independent test dataset from the same data source.

中文翻译：

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使用 PCA 和 mRMR 管理的纹理、方向和空间特征对阿尔茨海默病进行性神经退行性疾病进行基于机器学习的三阶段分类

阿尔茨海默病是一个备受关注的主题，在现代计算医学研究领域有足够的新发现空间，因为它是当今时代最严重和威胁生命的神经退行性疾病之一。这项研究的主要目标是开发一个系统，可以使用传统的机器学习方法自动检测阿尔茨海默病的三个阶段——阿尔茨海默氏症、轻度认知障碍和认知正常。从阿尔茨海默病神经成像计划收集的数据集包含上述三种类型的数据和标记图像，用于整个研究。在所提出的方法中，对比度受限的自适应直方图均衡化在特征计算之前处理定性视觉失真。从结构 MR 图像中识别出三种不同类型的特征，例如作为灰度共生矩阵、定向梯度直方图和局部聚合描述符向量的纹理、方向和空间特征。除此之外，主成分分析和最小冗余最大相关性对生成的特征集进行操作以进行降维并提供比较视角。对可用数据集进行的实验表明，所提出的方法优于其他值得注意的现有方法，用于阿尔茨海默病的多类检测，在功能集和模型方面实现了 94% 到 97% 的准确率。此外，在将发现应用于来自同一数据源的新独立测试数据集后，发现了重要的结果。