Efficient Algorithm for Distinction Mild Cognitive Impairment from Alzheimer’s Disease Based on Specific View FCM White Matter Segmentation and Ensemble Learning

Soheil Ahmadzadeh  Irandoost; Faeze Sadat  Mirafzali Saryazdi

doi:10.18502/fbt.v10i3.13158

Soheil Ahmadzadeh Irandoost Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Faeze Sadat Mirafzali Saryazdi Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

DOI: https://doi.org/10.18502/fbt.v10i3.13158

Keywords: Alzheimer's Disease; Fuzzy C Means; Ensemble.

Abstract

Purpose: Alzheimer's Disease (AD) is in the dementia group and is one of the most prevalent neurodegenerative disorders. Approximately 50 million people were affected in 2018, and that number is expected to triple by 2050. Several demographic properties, neuroimaging such as MRI, functional MRI (MRI), neuropsychiatric symptoms, and cognitive abilities are used to predict AD. Between existing characteristics, White Matter (WM) is a known marker for AD tracking, and WM segmentation in MRI based on clustering can be used to decrease the volume of data. Many algorithms have been developed to predict AD, but most concentrate on the distinction of AD from Cognitive Normal (CN), and fewer on the distinction of AD from Mild Cognitive Impairment (MCI), which has an important position in AD progression. In addition, there are not efficient algorithms with low computational costs and sufficient features in clinical use.

In this study, we provided a new, simple, and efficient methodology for classifying patients into AD and MCI patients and evaluated the effect of the view dimension of Fuzzy C means (FCM) in prediction with ensemble classifiers. This work was based on the segmentation of WM and extracting two groups of features.

Materials and Methods: We proposed our methodology in three steps; first, segmentation of WM from T1 MRI with FCM according to two specific viewpoints (3D and 2D). In the second step, two groups of features are extracted: approximate coefficients of Discrete Wavelet Transform (DWT) with three-level decomposition and statistical (mean, variance, skewness) features. In the final step, an ensemble classifier that is constructed with three simple classifiers, K-Nearest Neighbor (KNN), Decision Tree (DT), and Linear Discriminant Analysis (LDA), was used to distinguish MCI from AD.

Results: The proposed method has been evaluated by using 1,280 slices (samples) from 64 patients with MCI (32) and AD (32) of the ADNI dataset. The best performance is for the 3D viewpoint, and the accuracy, precision, and f1-score achieved from the methodology are 94.22%, 94.45%, and 94.21%, respectively, by using a ten-fold Cross-Validation (CV) strategy.

Conclusion: The experimental evaluation shows that WM segmentation increases the performance of the ensemble classifier; moreover, the 3D view FCM is better than the 2D view. According to the results, the proposed methodology has comparable performance for the detection of MCI from AD. The low computational cost algorithm and the three classifiers for generalization can be used in practical application by physicians in pre-clinical.