Binary Classification of Alzheimer's Disease Using sMRI Imaging Modality and Deep Learning
Binary Classification of Alzheimer's Disease Using sMRI Imaging Modality and Deep Learning
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DOI：
10.1007/s10278-019-00265-5
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发表时间：
2020-07-29
影响因子：
4.4
通讯作者：
Zhang, Qiu-Na
中科院分区：
文献类型：
--
作者：
Bin Tufail, Ahsan;Ma, Yong-Kui;Zhang, Qiu-Na
阿尔茨海默病（Alzheimer's disease，AD）是一种不可逆的神经退行性疾病，伴有进行性记忆和认知功能障碍。其早期诊断对于未来可能的治疗选择的发展至关重要。结构磁共振成像（sMRI）在帮助理解AD相关的解剖学变化方面发挥着重要作用，特别是在其早期阶段。传统的方法需要领域专家的专业知识，并提取精心挑选的特征，如灰质子结构，并训练分类器，以区分AD受试者和健康受试者。与这些方法不同的是，本文提出构建多个深度2D卷积神经网络（2D-CNN）来学习来自局部脑图像的各种特征，这些特征被组合起来，以进行AD诊断的最终分类。整个大脑图像通过两个迁移学习架构：Inception版本3和Xception，以及在可分离卷积层的帮助下构建的自定义卷积神经网络（CNN），该卷积层可以自动从成像数据中学习通用特征进行分类。我们的研究使用来自开放获取成像研究系列（OASIS）数据库的横截面T1加权结构MRI脑图像进行，以保持不同MRI扫描的大小和对比度。实验结果表明，迁移学习方法的性能超过了非迁移学习的方法，证明了这些方法对二进制AD分类任务的有效性。
Alzheimer's disease (AD) is an irreversible devastative neurodegenerative disorder associated with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for the development of possible future treatment option(s). Structural magnetic resonance images (sMRI) play an important role to help in understanding the anatomical changes related to AD especially in its early stages. Conventional methods require the expertise of domain experts and extract hand-picked features such as gray matter substructures and train a classifier to distinguish AD subjects from healthy subjects. Different from these methods, this paper proposes to construct multiple deep 2D convolutional neural networks (2D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. The whole brain image was passed through two transfer learning architectures; Inception version 3 and Xception, as well as a custom Convolutional Neural Network (CNN) built with the help of separable convolutional layers which can automatically learn the generic features from imaging data for classification. Our study is conducted using cross-sectional T1-weighted structural MRI brain images from Open Access Series of Imaging Studies (OASIS) database to maintain the size and contrast over different MRI scans. Experimental results show that the transfer learning approaches exceed the performance of non-transfer learning-based approaches demonstrating the effectiveness of these approaches for the binary AD classification task.