Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks.
Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks.
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DOI：
10.1186/s40708-018-0080-3
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发表时间：
2018-05-31
影响因子：
--
通讯作者：
Zhang Y
中科院分区：
文献类型：
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作者：
Islam J;Zhang Y
阿尔茨海默氏病是一种无法治愈的，进行性的神经脑疾病。早期发现阿尔茨海默病可以帮助进行适当的治疗，防止脑组织损伤。研究人员已经利用了几种统计和机器学习模型来诊断阿尔茨海默病。分析磁共振成像（MRI）是临床研究中阿尔茨海默病诊断的常见做法。由于阿尔茨海默病MRI数据和老年人的标准健康MRI数据的相似性，阿尔茨海默病的检测是严格的。最近，先进的深度学习技术已经在包括医学图像分析在内的众多领域成功展示了人类水平的性能。我们提出了一个深度卷积神经网络，用于使用大脑MRI数据分析进行阿尔茨海默病诊断。虽然大多数现有的方法进行二进制分类，我们的模型可以识别阿尔茨海默病的不同阶段，并获得上级性能的早期诊断。我们进行了大量的实验，以证明我们提出的模型优于开放获取系列成像研究数据集的比较基线。
Alzheimer’s disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer’s disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer’s disease diagnosis. Analyzing magnetic resonance imaging (MRI) is a common practice for Alzheimer’s disease diagnosis in clinical research. Detection of Alzheimer’s disease is exacting due to the similarity in Alzheimer’s disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer’s disease diagnosis using brain MRI data analysis. While most of the existing approaches perform binary classification, our model can identify different stages of Alzheimer’s disease and obtains superior performance for early-stage diagnosis. We conducted ample experiments to demonstrate that our proposed model outperformed comparative baselines on the Open Access Series of Imaging Studies dataset.