The automatic diagnosis of Alzheimer’s disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer’s disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer’s symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting of -nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer’s severity. The relationship between Alzheimer’s patients’ functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Examination score, including low, mild, moderate, and severe categories. Results show that the accuracy of the KNN, SVM, DT, LDA, RF, and presented CNN method is 77.5%, 85.8%, 91.7%, 79.5%, 85.1%, and 96.7%, respectively. Moreover, for the presented CNN architecture, the sensitivity of low, mild, moderate, and severe status of Alzheimer patients is 98.1%, 95.2%,89.0%, and 87.5%, respectively. Based on the findings, the presented CNN architecture classifier outperforms other methods and can diagnose the severity and stages of Alzheimer’s disease with maximum accuracy.

中文翻译：

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使用鲁棒的多任务特征提取方法和卷积神经网络 (CNN) 通过 fMRI 图像诊断阿尔茨海默病的严重程度

阿尔茨海默病的自动诊断对人类健康具有重要作用，尤其是在其早期阶段。因为它是一种神经退行性疾病，阿尔茨海默病似乎有很长的潜伏期。因此，分析阿尔茨海默病不同阶段的症状至关重要。在本文中，分类是通过几种机器学习方法完成的，包括-最近邻 (KNN)、支持向量机 (SVM)、决策树 (DT)、线性判别分析 (LDA) 和随机森林 (RF)。此外，还提出了新颖的卷积神经网络 (CNN) 架构来诊断阿尔茨海默病的严重程度。研究阿尔茨海默病患者的功能磁共振成像 (fMRI) 图像与其在 MMSE 上的分数之间的关系以实现目标。特征提取是基于鲁棒的多任务特征学习算法进行的。严重程度也是根据简易精神状态检查分数计算的，包括低、轻度、中度和重度类别。结果表明，KNN、SVM、DT、LDA、RF 和提出的 CNN 方法的准确率分别为 77.5%、85.8%、91.7%、79.5%、85.1% 和 96.7%。此外，对于所提出的 CNN 架构，灵敏度低，阿尔茨海默病患者的轻度、中度和重度状态分别为 98.1%、95.2%、89.0% 和 87.5%。基于这些发现，所提出的 CNN 架构分类器优于其他方法，并且可以最准确地诊断阿尔茨海默病的严重程度和阶段。