Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.

中文翻译：

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增强阿尔茨海默病分类的机器学习方法比较

阿尔茨海默病 (AD) 是一种进行性神经退行性疾病，占所有痴呆病例的近 60%。近年来，该病的发病率迅速增加。目前全世界约有 4680 万人患有 AD。目前缺乏逆转或阻止 AD 进展的有效治疗方法凸显了疾病预防和早期诊断的重要性。脑结构磁共振成像（MRI）已广泛用于 AD 检测，因为它可以显示形态差异和脑结构变化。在这项研究中，我们构建了三个基于机器学习的 MRI 数据分类器来预测 AD 并推断有助于疾病发生和进展的大脑区域。然后，我们系统地比较了分别基于支持向量机（SVM）、3D 超深卷积网络（VGGNet）和 3D 深度残差网络（ResNet）构建的三种不同的分类器。为了提高深度学习分类器的性能，我们应用了迁移学习策略。预训练模型的权重被转移并采用作为我们模型的初始权重。转移学习到的特征显着减少了训练时间并提高了网络效率。SVM、VGGNet 和 ResNet 分类器对 AD 受试者与老年对照受试者的分类准确率分别为 90%、95% 和 95%。通过利用 3D-VGGNet 和 3D-ResNet 模型学习到的空间信息，采用梯度加权类激活映射 (Grad-CAM) 来显示对 AD 分类贡献最大的判别区域。结果图始终突出显示了几个与疾病相关的大脑区域，特别是小脑，这是目前 AD 研究中相对被忽视的大脑区域。总体而言，我们的比较表明，与其他两种方法相比，ResNet 模型提供了最佳的分类性能以及更准确的大脑中疾病相关区域的定位。