The analysis of neuroimaging data is frequently used to assist the diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) or Parkinson's disease (PD) and has become a routine procedure in the clinical practice. During the past decade, the pattern recognition community has proposed a number of machine learning-based systems that automatically analyse neuroimaging data in order to improve the diagnosis. However, the high dimensionality of the data is still a challenge and there is room for improvement. The development of novel classification frameworks as TensorFlow, recently released as open source by Google Inc., represents an opportunity to continue evolving these systems. In this work, we demonstrate several computer-aided diagnosis (CAD) systems based on Deep Neural Networks that improve the diagnosis for AD and PD and outperform those based on classical classifiers. In order to address the small sample size problem we evaluate two dimensionality reduction algorithms based on Principal Component Analysis and Non-Negative Matrix Factorization (NNMF), respectively. The performance of developed CAD systems is assessed using 4 datasets with neuroimaging data of different modalities.

中文翻译：

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使用深度神经网络以及降维技术来帮助诊断神经退行性疾病。

神经影像数据的分析通常用于辅助诊断神经退行性疾病，例如阿尔茨海默氏病（AD）或帕金森氏病（PD），并已成为临床实践中的常规程序。在过去的十年中，模式识别社区提出了许多基于机器学习的系统，这些系统可以自动分析神经影像数据以改善诊断。但是，数据的高维度仍然是一个挑战，还有改进的空间。Google Inc.最近以开源形式发布了TensorFlow等新颖的分类框架，这为继续发展这些系统提供了机会。在这项工作中 我们展示了几种基于深度神经网络的计算机辅助诊断（CAD）系统，它们可以改善AD和PD的诊断水平，并优于传统分类器。为了解决样本量小的问题，我们分别基于主成分分析和非负矩阵分解（NNMF）评估了二维降维算法。已开发的CAD系统的性能使用4个数据集进行评估，这些数据集具有不同形式的神经影像数据。