论文标题
使用3DMGNET预测阿尔茨海默氏病
Predicting Alzheimer's Disease Using 3DMgNet
论文作者
论文摘要
阿尔茨海默氏病（AD）是大脑的不可逆神经产生疾病。该疾病可能导致记忆力丧失，难以交流和迷失方向。为了诊断阿尔茨海默氏病，通常需要一系列量表来在临床上评估诊断，这不仅增加了医生的工作量，而且还使诊断结果高度主观。因此，对于阿尔茨海默氏病，成像意味着找到早期诊断标记物已成为当务之急。 在本文中，我们提出了一种新颖的3DMGNET结构，该结构是隔离的综合和卷积神经网络的统一框架，用于诊断阿尔茨海默氏病（AD）。使用开放数据集（ADNI数据集）对模型进行训练，然后使用我们的较小数据集进行测试。最后，该模型的AD与NC分类达到了92.133％的精度，并显着降低了模型参数。
Alzheimer's disease (AD) is an irreversible neurode generative disease of the brain.The disease may causes memory loss, difficulty communicating and disorientation. For the diagnosis of Alzheimer's disease, a series of scales are often needed to evaluate the diagnosis clinically, which not only increases the workload of doctors, but also makes the results of diagnosis highly subjective. Therefore, for Alzheimer's disease, imaging means to find early diagnostic markers has become a top priority. In this paper, we propose a novel 3DMgNet architecture which is a unified framework of multigrid and convolutional neural network to diagnose Alzheimer's disease (AD). The model is trained using an open dataset (ADNI dataset) and then test with a smaller dataset of ours. Finally, the model achieved 92.133% accuracy for AD vs NC classification and significantly reduced the model parameters.
