The utilisation of magnetic resonance imaging (MRI) images for the automated detection of Alzheimer’s disease has garnered significant attention in recent years. This interest stems from the progress made in machine learning techniques and the possible application of such methods in the field of diagnostics. This study aims to evaluate the performance of 16 histogram-based image texture descriptors and features extracted from 18 pre-trained convolutional neural networks in characterising brain patterns observed in 2D slices of MRI images. The primary objective is to determine the most effective feature types for this task. The characteristics were taken from the magnetic resonance imaging (MRI) dataset given by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The study involved the calculation of features on 2D axial, coronal, and sagittal slices, followed by classification using five binary machine learning algorithms. The objective was to differentiate between individuals with normal cognitive function and those diagnosed with Alzheimer’s disease. The proposed methodology additionally facilitated the identification of specific brain areas to be selected for each axis, in order to achieve optimal accuracy. This involved determining the matching feature and classifier combinations.

近年来，利用磁共振成像（MRI）图像自动检测阿尔茨海默病引起了人们的广泛关注。这种兴趣源于机器学习技术的进步以及此类方法在诊断领域的可能应用。本研究旨在评估 16 个基于直方图的图像纹理描述符和从 18 个预训练的卷积神经网络中提取的特征在表征 MRI 图像 2D 切片中观察到的大脑模式时的性能。主要目标是确定此任务最有效的特征类型。这些特征取自阿尔茨海默病神经影像倡议 (ADNI) 提供的磁共振成像 (MRI) 数据集。该研究涉及二维轴向、冠状和矢状切片的特征计算，然后使用五种二进制机器学习算法进行分类。目的是区分具有正常认知功能的个体和被诊断患有阿尔茨海默病的个体。所提出的方法还有助于识别为每个轴选择的特定大脑区域，以实现最佳准确性。这涉及确定匹配特征和分类器组合。