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. Ten-fold 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.
Highlights
Neurodegenerative diseases have gained increasing attention in the past few decades; these include Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI), and others
Several objectives were addressed for detecting AD via a machine learning technique; namely convolutional neural network (CNN). the first objective is to search for the best values for the CNN hyperparameters that would maximize performance
The second objective is study if the diffusion maps would yield a good discrimination between different classes or fusion with other structural data will boost the performance
Summary
Neurodegenerative diseases have gained increasing attention in the past few decades; these include Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI), and others. Image based diagnosis of AD is important and required mainly to avoid subjective assessments [4]. Deep learning-based methods gives successful results in medical image analysis [5] due to flexible and efficient formulations [6]. In 2017, over 121 thousand people died from AD, in the United States, making it the sixth leading cause of death. Between the years 2000 and 2017, the number of deaths due to AD has increased by 145% [7]. By 2050, the number of people older than 60 years will be increased by 1.25 billion, equivalent to 22% of the global population, with 79% living in the world’s less developed countries [8]. The annual expenses for the disease per is around $868 and $3,109 per person in low-income and lower-to middle- income countries respectively [9]
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