Alzheimer's Disease Classification Based on Multi-feature Fusion

Abstract

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

A novel discriminant feature selection–based mutual information extraction from MR brain images for Alzheimer's stages detection and prediction

“…When the classifier is correctly trained, it may be used to identify individuals with a specific brain disorder. 7,8 Classification is a type of supervised learning in which the targets are also given the input data. 5,6 Magda et al 9 proposed one of the efficient machine learning-based approaches, wherein a computerized decision support system uses three classifiers: support vector machine (SVM), support vector regression, and k-nearest neighbor (KNN) on MRI.…”

“…In brain disorder detection, training data must include both individuals suffering from disorder and normal control (NC). When the classifier is correctly trained, it may be used to identify individuals with a specific brain disorder 7,8 . Classification is a type of supervised learning in which the targets are also given the input data 5,6 .…”

Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease

“…Only when AD is diagnosed in the early stage can it be possible to slow down or inhibit the progression [ 15 ]. In recent years, there is a large amount of research on the classification and prediction of early AD using traditional machine learning methods [ 16 ], and the classification and prediction of early AD using deep learning technology is a common occurrence [ 17 ]. For example, Folego et al (2020) [ 18 ] used CNNs to process MRI images, and the classification accuracy of AD and HC reached 0.97.…”

Sleep EEG-Based Approach to Detect Mild Cognitive Impairment

“…C is a punishment parameter that is used to penalize mistakes during training, b is a bias term, w is the weight applied for input data x i . The kernel function φ( x ) is a nonlinear transformation function that maps the input vectors into a high-dimensional feature space ( Madusanka et al, 2019 ).…”