Memory related issues in brain are mainly caused by Alzheimer disease (AD) which is the most common form of dementia. This disease must be diagnosed in its prodromal stage known as Mild Cognitive Impairment (MCI) also it needs an accurate detection and classification technique. In this paper, a computer-aided diagnosis (CAD) system is implemented on Magnetic resonance imaging (MRI) data from ADNI database. This disease highly affects the Hippocampus and cerebrum regions which are normally found in the grey matter region of brain. At first, MNI/ICBM atlas space of every three dimensional MRI images are constructed using normalization procedure, then grey matter region of brain is extracted. Subsequently, feature extraction is done by two dimensional Gabor filter in three scales and eight orientations. Then, the proposed optimal Deep Neural Network (DNN) classifier is used to classify the images as Cognitive normal (CN), Alzheimer disease (AD), and Mild Cognitive Impairment (MCI). Here, DNN classifier is optimized by selecting optimal weight parameter using Enhanced Squirrel Search Algorithm. The experimental results prove an efficiency of the proposed method using MR images. The proposed algorithm beats existing techniques in terms of accuracy, sensitivity, and specificity.

大脑中与记忆有关的问题主要由阿尔茨海默氏病（AD）引起，这是痴呆症的最常见形式。这种疾病必须在其前驱阶段被诊断为轻度认知障碍（MCI），并且还需要一种准确的检测和分类技术。本文基于ADNI数据库中的磁共振成像（MRI）数据，实现了计算机辅助诊断（CAD）系统。这种疾病严重影响了通常在大脑灰质区域中发现的海马和大脑区域。首先，使用归一化程序构造每3维MRI图像的MNI / ICBM图集空间，然后提取大脑的灰质区域。随后，通过二维Gabor滤波器以三个比例和八个方向进行特征提取。然后，拟议的最佳深度神经网络（DNN）分类器用于将图像分类为正常认知（CN），阿尔茨海默病（AD）和轻度认知障碍（MCI）。在此，通过使用增强型松鼠搜索算法选择最佳权重参数来优化DNN分类器。实验结果证明了该方法的有效性。提出的算法在准确性，灵敏性和特异性方面击败了现有技术。