The automated magnetic resonance imaging (MRI) processing techniques are gaining more importance in Alzheimer disease (AD) recognition, because it effectively diagnosis the pathology of the brain. Currently, computer aided diagnosis based on image analysis is an emerging tool to support AD diagnosis. In this research study, a new system is developed for enhancing the performance of AD recognition. Initially, the brain images were acquired from three online datasets and one real-time dataset such as AD Neuroimaging Initiative (ADNI), Minimal Interval Resonance Imaging in AD (MIRIAD), and Open Access Series of Imaging Studies (OASIS) and National Institute of Mental Health and Neuro Sciences (NIMHANS). Then, adaptive histogram equalization (AHE) and grey wolf optimization based clustering algorithm (GWOCA) were applied for denoising and segmenting the brain tissues; grey matter (GM), cerebro-spinal fluid (CSF), and white matter (WM) from the acquired images. After segmentation, the feature extraction was performed by utilizing dual tree complex wavelet transform (DTCWT), local ternary pattern (LTP) and Tamura features to extract the feature vectors from the segmented brain tissues. Then, ReliefF methodology was used to select the active features from the extracted feature vectors. Finally, the selected active feature values were classified into three classes [AD, normal and mild cognitive impairment (MCI)] utilizing deep neural network (DNN) classifier. From the simulation result, it is clear that the proposed framework achieved good performance in disease classification and almost showed 2.2–6% enhancement in accuracy of all four datasets.

自动磁共振成像 (MRI) 处理技术在阿尔茨海默病 (AD) 识别中越来越重要，因为它可以有效地诊断大脑的病理。目前，基于图像分析的计算机辅助诊断是支持AD诊断的新兴工具。在这项研究中，开发了一种新系统来提高 AD 识别的性能。最初，大脑图像是从三个在线数据集和一个实时数据集获取的，例如 AD 神经影像学倡议 (ADNI)、AD 中的最小间隔共振成像 (MIRIAD) 和开放获取系列影像研究 (OASIS) 和美国国立卫生研究院。心理健康和神经科学（NIMHANS）。然后，应用自适应直方图均衡（AHE）和基于灰狼优化的聚类算法（GWOCA）对脑组织进行去噪和分割；从获取的图像中提取灰质 (GM)、脑脊液 (CSF) 和白质 (WM)。分割后，利用双树复小波变换（DTCWT）、局部三元模式（LTP）和田村特征进行特征提取，从分割后的脑组织中提取特征向量。然后，使用 ReliefF 方法从提取的特征向量中选择活动特征。最后，利用深度神经网络 (DNN) 分类器将选定的活动特征值分为三类 [AD、正常和轻度认知障碍 (MCI)]。从仿真结果来看，