阿尔茨海默病(AD)在全球范围内构成重大健康风险,尤其是老年人。最近的研究强调了它的患病率,超过50%的日本老年人面临痴呆症的终生风险,主要归因于AD。作为痴呆症最常见的形式,AD逐渐侵蚀脑细胞,导致严重的神经衰退.在这种情况下,开发自动AD检测系统非常重要,许多研究人员一直在努力通过利用深度学习(DL)技术的进步来开发AD检测系统,在各个领域都显示出了有希望的结果,包括医学图像分析。然而,由于与训练分层卷积神经网络(CNN)相关联的复杂性,用于AD检测的现有方法通常遭受有限的性能。在本文中,我们引入了一种基于残差函数的多阶段深度神经网络架构,以解决现有AD检测方法的局限性。受残差网络(ResNets)在图像分类任务中的成功启发,我们拟议的系统包括五个阶段,每个都明确公式化,以增强特征有效性,同时保持模型深度。在特征提取之后,应用基于深度学习的特征选择模块来减轻过拟合,结合批量标准化,dropout和完全连接的层。随后,基于机器学习(ML)的分类算法,包括支持向量机(SVM),随机森林(RF)和SoftMax,用于分类任务。对三个基准数据集进行综合评价,即ADNI1:完成1年1.5T,MIRAID和OASISKaggle,证明了我们提出的模型的有效性。令人印象深刻的是,我们的模型达到了99.47%的准确率,ADNI1的99.10%和99.70%:完成1年1.5T,MIRAID和OASIS数据集,分别,在二进制类问题中优于现有系统。我们提出的模型代表了AD分析领域的重大进步。
Alzheimer\'s Disease (AD) poses a significant health risk globally, particularly among the elderly population. Recent studies underscore its prevalence, with over 50% of elderly Japanese facing a lifetime risk of dementia, primarily attributed to AD. As the most prevalent form of dementia, AD gradually erodes brain cells, leading to severe neurological decline. In this scenario, it is important to develop an automatic AD-detection system, and many researchers have been working to develop an AD-detection system by taking advantage of the advancement of deep learning (DL) techniques, which have shown promising results in various domains, including medical image analysis. However, existing approaches for AD detection often suffer from limited performance due to the complexities associated with training hierarchical convolutional neural networks (CNNs). In this paper, we introduce a novel multi-stage deep neural network architecture based on residual functions to address the limitations of existing AD-detection approaches. Inspired by the success of residual networks (ResNets) in image-classification tasks, our proposed system comprises five stages, each explicitly formulated to enhance feature effectiveness while maintaining model depth. Following feature extraction, a deep learning-based feature-selection module is applied to mitigate overfitting, incorporating batch normalization, dropout and fully connected layers. Subsequently, machine learning (ML)-based classification algorithms, including Support Vector Machines (SVM), Random Forest (RF) and SoftMax, are employed for classification tasks. Comprehensive evaluations conducted on three benchmark datasets, namely ADNI1: Complete 1Yr 1.5T, MIRAID and OASIS Kaggle, demonstrate the efficacy of our proposed model. Impressively, our model achieves accuracy rates of 99.47%, 99.10% and 99.70% for ADNI1: Complete 1Yr 1.5T, MIRAID and OASIS datasets, respectively, outperforming existing systems in binary class problems. Our proposed model represents a significant advancement in the AD-analysis domain.