Al-Khuzaie Fanar E K, Bayat Oguz, Duru Adil D
Graduate School of Science and Engineering, Altinbas University, Istanbul, Turkey.
Department of Physical Education and Sports Teaching, University of Marmara, Istanbul, Turkey.
Appl Bionics Biomech. 2021 Feb 2;2021:6690539. doi: 10.1155/2021/6690539. eCollection 2021.
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.
有多种脑部异常会导致大脑不同部位发生变化。阿尔茨海默病是一种使脑细胞退化从而导致记忆衰弱的慢性疾病。诸如健忘和困惑等认知心理问题是阿尔茨海默病患者最重要的特征之一。在文献中，已经介绍了几种用于该疾病诊断的图像处理技术以及机器学习策略。本研究旨在基于脑部磁共振成像识别阿尔茨海默病的存在。我们采用深度学习方法，根据使用磁共振成像收集的二维解剖切片，区分阿尔茨海默病患者和健康患者。之前的大多数研究基于三维卷积神经网络的实现，而我们将二维切片用作卷积神经网络的输入。本研究的数据集来自OASIS网站。我们使用二维切片训练卷积神经网络结构，以展示我们命名为阿尔茨海默网络（AlzNet）的深度网络权重。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，如层数、滤波器数量和丢弃率。在使用许多性能指标评估所提出的AlzNet之后，结果很有趣。
