Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks

Abstract

Machine learning algorithms are currently being implemented in an escalating manner to classify and/or predict the onset of some neurodegenerative diseases; including Alzheimer's Disease (AD); this could be attributed to the fact of the abundance of data and powerful computers. The objective of this work was to deliver a robust classification system for AD and Mild Cognitive Impairment (MCI) against healthy controls (HC) in a low-cost network in terms of shallow architecture and processing. In this study, the dataset included was downloaded from the Alzheimer's disease neuroimaging initiative (ADNI). The classification methodology implemented was the convolutional neural network (CNN), where the diffusion maps, and gray-matter (GM) volumes were the input images. The number of scans included was 185, 106, and 115 for HC, MCI and AD respectively. Tenfold cross-validation scheme was adopted and the stacked mean diffusivity (MD) and GM volume produced an AUC of 0.94 and 0.84, an accuracy of 93.5% and 79.6%, a sensitivity of 92.5% and 62.7%, and a specificity of 93.9% and 89% for AD/HC and MCI/HC classification respectively. This work elucidates the impact of incorporating data from different imaging modalities; i.e. structural Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI), where deep learning was employed for the aim of classification. To the best of our knowledge, this is the first study assessing the impact of having more than one scan per subject and propose the proper maneuver to confirm the robustness of the system. The results were competitive among the existing literature, which paves the way for improving medications that could slow down the progress of the AD or prevent it.

Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

“…With respect to conventional ML methods, deep learning algorithms require little or no image pre-processing, and can automatically infer an optimal representation of the data from the raw images without requiring prior feature selection, thus resulting in a more objective and less biased process [67]. Few papers on the application of deep learning approaches, and in particular convolutional neural networks, in the classification or prediction of AD using DTI imaging data have been recently published achieving good results [68,69]. More comprehensive studies are needed to evaluate the advantages of these methods compared with more traditional approaches.…”

Detection of gray matter microstructural changes in Alzheimer’s disease continuum using fiber orientation

“…A recent study that adopted a logistic regression model with neurite density index, orientation dispersion index, and CTh as features reported 0.72 AUC for CN and MCI and 0.91 AUC for CN and AD [ 35 ]. Other studies employing whole MD and gray matter map reported 79.6% accuracy with 0.84 AUC for CN and MCI and 93.5% with 0.94 AUC to CN and AD [ 36 ], 76% with 0.83 AUC for NC and AD [ 37 ].…”

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

“…As a result, recent trends in AD diagnosis include the use of DL-based approaches. DL-based [7,11,12,[19][20][21] studies consider multimodal information for classifying AD and mAD from NC. The studies [7,22,23] use 3D patches from the whole brain to train and test a CNN model.…”