Machine learning and deep learning play a crucial role in identification of various diseases like neurological, skin, eyes, blood and cancers. The deep learning algorithms can be performed promising for prediction of Alzheimer’s disease using MRI scans. Alzheimer disease becoming more common in the people (age 65 years or above). The disease becomes severe before the symptoms appear and causes brain disorder that cannot be cured by medicines and other therapies and treatments. So the early diagnosis is necessary to slow down its progression. Detection and prevention of Alzheimer disease is one of the active research area for the researchers nowadays. In this paper, we employed architectures of convoutional networks using freeze features extracted from source data set ImageNet for binary and ternary classification. All experiments were carried out using Alzheimer’s disease national initiative (ADNI) data set consisting of MRI scans. The performance of proposed system demonstrates for classification of Alzheimer’s disease versus mild cognitive impairment, normal controls versus mild cognitive impairment, and cognitive normal versus Alzheimer’s disease. The results of proposed study show that VGG architecture outperforms the state-of-the-art techniques and number of architectures of conveNet (AlexNet, GoogLeNet, ResNet, DenseNet, Inceptionv3, InceptionResNet) in Alzheimer’s disease detection, and achieves an identification test set accuracy of 99.27% (MCI/AD), 98.89% (AD/CN) and 97.06% (MCI/CN).

机器学习和深度学习在识别各种疾病（例如神经系统疾病，皮肤，眼睛，血液和癌症）中起着至关重要的作用。可以使用MRI扫描执行深度学习算法，有望预测阿尔茨海默氏病。阿尔茨海默氏病在人们（65岁或以上）中变得越来越普遍。该病在症状出现之前就变得很严重，并导致无法通过药物和其他疗法和疗法治愈的脑部疾病。因此，早期诊断对于减慢其进展是必要的。阿尔茨海默氏病的检测和预防是当今研究人员的活跃研究领域之一。在本文中，我们采用对流网络的体系结构，该体系使用从源数据集ImageNet中提取的冻结特征进行二进制和三进制分类。所有实验均使用由MRI扫描组成的国家阿尔茨海默氏病倡议（ADNI）数据集进行。拟议系统的性能证明了阿尔茨海默氏病与轻度认知障碍的分类，正常对照与轻度认知障碍的分类以及认知正常与阿尔茨海默氏病的分类。拟议的研究结果表明，VGG架构在阿尔茨海默氏病的检测中胜过conveNet（AlexNet，GoogLeNet，ResNet，DenseNet，Inceptionv3，InceptionResNet）的最新技术和架构数量，并达到了鉴定测试集的准确性分别为99.27％（MCI / AD），98.89％（AD / CN）和97.06％（MCI / CN）。拟议系统的性能证明了阿尔茨海默氏病与轻度认知障碍的分类，正常对照与轻度认知障碍的分类以及认知正常与阿尔茨海默氏病的分类。拟议的研究结果表明，VGG架构在阿尔茨海默氏病的检测中胜过conveNet（AlexNet，GoogLeNet，ResNet，DenseNet，Inceptionv3，InceptionResNet）的最新技术和架构数量，并达到了鉴定测试集的准确性分别为99.27％（MCI / AD），98.89％（AD / CN）和97.06％（MCI / CN）。拟议系统的性能证明了阿尔茨海默氏病与轻度认知障碍的分类，正常对照与轻度认知障碍的分类以及认知正常与阿尔茨海默氏病的分类。拟议的研究结果表明，VGG架构在阿尔茨海默氏病的检测中胜过conveNet（AlexNet，GoogLeNet，ResNet，DenseNet，Inceptionv3，InceptionResNet）的最新技术和架构数量，并达到了鉴定测试集的准确性分别为99.27％（MCI / AD），98.89％（AD / CN）和97.06％（MCI / CN）。