Alzheimer's disease (AD) is a widespread neurodegenerative disease that causes 60–80% of all dementias and has a large economic impact in developed countries. For early-stage AD detection, volumetric measures of magnetic resonance imaging (MRI) have proven to be a benchmark method. To detect potential cases, existing methods combine health records, neuropsychological testing, and MRI, although learning implementation is inconsistently used and has low sensitivity and specificity. Furthermore, numerous classification approaches for diagnosing AD have been suggested with differing complexity. Thus, we have introduced our novel AD diagnosis model with two main phases such as proposed feature extraction and classification. In the first phase, the gray-level co-occurrence matrix (GLCM), Haralick features as well as proposed geometric Haralick features known as geometric correlation and variance are extracted. In the second phase, an optimized deep convolutional neural network (DCNN) is utilized for classification. To make the prediction more accurate, the weight and the activation function of DCNN are optimally chosen by a new hybrid model termed as Combined Gray Wolf and Dragon Updating (CG-DU). At last, the superiority of the adopted scheme is validated in terms of performance analysis, convergence analysis, box plot analysis, and computation time analysis. Especially, the proposed model achieves a mean accuracy of 0.98795, sensitivity of 0.98671, and specificity of 0.99429. Moreover, the computation time of the CG-DU model is 2.92%, and 0.14% superior to existing GWO and DA methods respectively.

中文翻译：

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从 MRI 图像自动评估阿尔茨海默病诊断：元启发式辅助深度学习模型

阿尔茨海默病 (AD) 是一种广泛存在的神经退行性疾病，它会导致 60-80% 的痴呆症，并在发达国家产生巨大的经济影响。对于早期 AD 检测，磁共振成像 (MRI) 的体积测量已被证明是一种基准方法。为了检测潜在病例，现有方法结合了健康记录、神经心理学测试和 MRI，尽管学习实施的使用不一致并且敏感性和特异性较低。此外，已经提出了具有不同复杂性的多种诊断 AD 的分类方法。因此，我们介绍了具有两个主要阶段的新型 AD 诊断模型，例如提出的特征提取和分类。在第一阶段，灰度共生矩阵（GLCM），提取了 Haralick 特征以及被称为几何相关性和方差的提出的几何 Haralick 特征。在第二阶段，使用优化的深度卷积神经网络 (DCNN) 进行分类。为了使预测更准确，DCNN 的权重和激活函数通过一种新的混合模型进行优化选择，称为灰狼和龙联合更新 (CG-DU)。最后从性能分析、收敛性分析、箱线图分析、计算时间分析等方面验证了所采用方案的优越性。特别是，该模型的平均准确度为 0.98795，敏感性为 0.98671，特异性为 0.99429。此外，CG-DU 模型的计算时间分别优于现有的 GWO 和 DA 方法 2.92% 和 0.14%。