Using deep neural networks along with dimensionality reduction techniques to assist the diagnosis of neurodegenerative disorders

Abstract

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.

Keywords: Alzheimer’s disease; Multivariate analysis; Parkinson’s disease; TensorFlow; deep neural networks; machine learning; non-negative matrix factorization; principal component analysis.

Figure 1.

Intermediate accuracies obtained in the cross-validation procedure. Blue boxes and circled dots represent accuracies’ range and median, respectively.

Figure 2.

ROC curves for the best systems for each database. The AUC measure is included in the legend. Datasets 1, 2, 3 and 4 contain Tc-ECD SPECT (AD diagnosis), DaTSCAN (PS diagnosis), F-DMFP-PET (PD diagnosis) and F-FDG-PET (AD diagnosis) data, respectively.