There are many kinds of brain abnormalities that cause changes in different parts of the brain. Alzheimer’s disease is a chronic condition that degenerates the cells of the brain leading to memory asthenia. Cognitive mental troubles such as forgetfulness and confusion are one of the most important features of Alzheimer’s patients. In the literature, several image processing techniques, as well as machine learning strategies, were introduced for the diagnosis of the disease. This study is aimed at recognizing the presence of Alzheimer’s disease based on the magnetic resonance imaging of the brain. We adopted a deep learning methodology for the discrimination between Alzheimer’s patients and healthy patients from 2D anatomical slices collected using magnetic resonance imaging. Most of the previous researches were based on the implementation of a 3D convolutional neural network, whereas we incorporated the usage of 2D slices as input to the convolutional neural network. The data set of this research was obtained from the OASIS website. We trained the convolutional neural network structure using the 2D slices to exhibit the deep network weightings that we named as the Alzheimer Network (AlzNet). The accuracy of our enhanced network was 99.30%. This work investigated the effects of many parameters on AlzNet, such as the number of layers, number of filters, and dropout rate. The results were interesting after using many performance metrics for evaluating the proposed AlzNet.

中文翻译：

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通过卷积神经网络使用 2D MRI 切片诊断阿尔茨海默病

有很多种大脑异​​常会导致大脑不同部位的变化。阿尔茨海默病是一种慢性疾病，会导致大脑细胞退化，导致记忆力减退。健忘和混乱等认知精神问题是阿尔茨海默病患者最重要的特征之一。文献中介绍了几种图像处理技术以及机器学习策略用于疾病的诊断。这项研究旨在根据大脑磁共振成像来识别阿尔茨海默病的存在。我们采用深度学习方法，通过磁共振成像收集的 2D 解剖切片来区分阿尔茨海默病患者和健康患者。之前的大多数研究都是基于 3D 卷积神经网络的实现，而我们将 2D 切片的使用合并为卷积神经网络的输入。本研究的数据集来自OASIS网站。我们使用 2D 切片训练卷积神经网络结构，以展示深度网络权重，我们将其命名为阿尔茨海默网络 (AlzNet)。我们增强网络的准确率为 99.30%。这项工作研究了许多参数对 AlzNet 的影响，例如层数、滤波器数量和 dropout 率。使用许多性能指标来评估所提出的 AlzNet 后，结果很有趣。