The build-up of beta-amyloid and rapid spread of tau proteins in the brain cause the death of neurons, leading to Alzheimer's disease (AD). AD is a form of dementia, and the symptoms include memory loss and decision-making difficulties. Current advanced diagnostic modalities are costly or unable to detect the histopathological features of AD. Hence a computational intelligence tool (CIT) for AD diagnosis is proposed in this study. The magnetic resonance images (MRI) of the brain are pre-processed using an adaptive histogram, and decomposed into four IMFS using bidirectional empirical mode decomposition (BEMD). Local binary patterns (LBP) are then computed per IMF, and the histograms are concatenated. Adaptive synthetic sampling (ADASYN) is applied to balance the dataset and Student's t-test is utilized for selection of highly significant features, within each fold for ten-fold validation. Amongst other classifiers, SVM-Poly 1 and random forest(RF) were employed for classification, yielding the highest accuracy of 93.9% each. Our study concludes that the recommended CIT is useful for the automatic classification of AD versus normal MRI imagery in hospitals.

β-淀粉样蛋白的堆积和tau蛋白在大脑中的快速传播会导致神经元死亡，从而导致阿尔茨海默氏病（AD）。AD是痴呆的一种形式，其症状包括记忆力减退和决策困难。当前的高级诊断方式昂贵或无法检测AD的组织病理学特征。因此，本研究提出了一种用于AD诊断的计算智能工具（CIT）。使用自适应直方图对大脑的磁共振图像（MRI）进行预处理，并使用双向经验模式分解（BEMD）将其分解为四个IMFS。然后，每个IMF计算局部二进制模式（LBP），并将直方图连接起来。自适应合成采样（ADASYN）用于平衡数据集和学生的t-test用于选择高度重要的特征，每次折叠内均进行十次验证。在其他分类器中，使用SVM-Poly 1和随机森林（RF）进行分类，每个分类器的最高准确性为93.9％。我们的研究得出的结论是，推荐的CIT对于医院中AD与正常MRI图像的自动分类很有用。