Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

Abstract

Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment (MCI), and healthy control (HC) subjects using a support vector machine (SVM), an import vector machine (IVM), and a regularized extreme learning machine (RELM). The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer's disease neuroimaging initiative (ADNI) datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

White Matter Network Alterations in Alzheimer’s Disease Patients

“…Different imaging models, such as electroencephalography (EEG) [9], functional magnetic resonance imaging (fMRI) [10] and positron emission tomography (PET) [11], have been used to study the progression of disease. The majority of studies have investigated using the structural magnetic resonance imaging (MRI) [12][13][14] that assists in the visualization of degenerative histological changes caused by neurological disorders. The feature extracted from MRI is typically grey matter volumes.…”

Random Forest Feature Selection, Fusion and Ensemble Strategy: Combining Multiple Morphological MRI Measures to Discriminate among healthy elderly, MCI, cMCI and Alzheimer's disease patients: from the Alzheimer’s disease neuroimaging initiative (ADNI) database

“…for HC-MCI-AD using a regularized extreme learning machine and PCA for feature selection (Lama et al, 2017). The whole approach was based on an internal cross-validation scheme without attempting to classify a second external blind dataset.…”

The Geometry of the Generalized Gamma Manifold and an Application to Medical Imaging

“…Magnetic Resonance Imaging (MRI) seeks to identify, localize and measure different parts of the anatomy of the central nervous system. It is of common use for the diagnosis of neurodegenerative diseases such as Alzheimer's disease [1][2][3]. The brain atrophy can be estimated from the measure of the cortical thickness [4,5].…”

“…In the medical field, the use of medical imaging techniques, such as Magnetic Resonance Imaging (MRI), is particularly important for measuring brain activity in different parts of the anatomy of the central nervous system. This technique is of major interest in the context of neurodegenerative diseases such as Alzheimer's disease [1][2][3]. By measuring the cortical thickness of the brain, it is possible to estimate the brain atrophy that is considered as a crucial marker of neurodegeneration in Alzheimer's disease [4,5].…”