Islamic Azad University, Central Tehran Branch (IAUCTB), Department of Electrical and Electronics Engineering, Tehran, Iran.
Department of Electrical Engineering, University of Applied Science and Technology, Bushehr, Iran.
Comput Intell Neurosci. 2022 May 11;2022:9554768. doi: 10.1155/2022/9554768. eCollection 2022.
Alzheimer's disease (AD) is a type of dementia that affects the elderly population. A machine learning (ML) system has been trained to recognize particular patterns to diagnose AD using an algorithm in an ML system. As a result, developing a feature extraction approach is critical for reducing calculation time. The input image in this article is a Two-Dimensional Discrete Wavelet (2D-DWT). The Time-Dependent Power Spectrum Descriptors (TD-PSD) model is used to represent the subbanded wavelet coefficients. The principal property vector is made up of the characteristics of the TD-PSD model. Based on classification algorithms, the collected characteristics are applied independently to present AD classifications. The categorization is used to determine the kind of tumor. The TD-PSD method was used to extract wavelet subbands features from three sets of test samples: moderate cognitive impairment (MCI), AD, and healthy controls (HC). The outcomes of three modes of classic classification methods, including KNN, SVM, Decision Tree, and LDA approaches, are documented, as well as the final feature employed in each. Finally, we show the CNN architecture for AD patient classification. Output assessment is used to show the results. Other techniques are outperformed by the given CNN and DT.
阿尔茨海默病（AD）是一种影响老年人群的痴呆症。已经训练了一个机器学习（ML）系统，使用 ML 系统中的算法来识别特定模式以诊断 AD。因此，开发特征提取方法对于减少计算时间至关重要。本文中的输入图像是二维离散小波（2D-DWT）。时变功率谱描述符（TD-PSD）模型用于表示子带小波系数。主属性向量由 TD-PSD 模型的特征组成。基于分类算法，将收集的特征分别应用于呈现 AD 分类。分类用于确定肿瘤的类型。TD-PSD 方法从三组测试样本中提取小波子带特征：中度认知障碍（MCI）、AD 和健康对照（HC）。记录了三种经典分类方法模式（KNN、SVM、决策树和 LDA 方法）的结果，以及每种方法最终使用的特征。最后，我们展示了用于 AD 患者分类的 CNN 架构。输出评估用于显示结果。所给的 CNN 和 DT 优于其他技术。
