Classification of Brain MRI for Alzheimer’s Disease Detection Based on Ensemble Machine Learning


avatar Soheil Ahmadzadeh Irandoost 1 , * , avatar Fatemeh Asadi 2

Department of Medical Physics and Medical Engineering, Medical School, Tehran University of Medical Science, Tehran, Iran
Department of Medical Physics and Biomedical Engineering, Medicine College, Tehran University of Medical Science, Tehran, Iran

how to cite: Ahmadzadeh Irandoost S , Asadi F. Classification of Brain MRI for Alzheimer’s Disease Detection Based on Ensemble Machine Learning. Innov J Radiol. 2019;16(Special Issue):e99157.



Machine learning is now a powerful tool to help improve medical disorders diagnosis. One of its critical applications is the classification or clustering of neurodegenerative disease by pattern recognition methods based on biomedical signals and medical images. Early detection of these diseases is always useful and vital. In this study, we focused on Alzheimer’s disease (AD) as a type of dementia leading to problems with memory, thinking, and behavior. This disease was named after Dr. Alois Alzheimer in 1906 when he inspected a female patient who died of an unusual mental illness. According to recent studies, four stages are introduced for AD, including pre-dementia, early AD, moderate AD, and advanced AD. There are several methods for AD diagnosis that include mental status evaluation, physical exam, and neurological exam, based on different imaging techniques such as magnetic resonance imaging (MRI). Several methods have been introduced until now for the classification and detection of AD using machine learning algorithms, such as the classification of AD with discrete wavelet transform (DWT) and single linear discriminant analysis (LDA) classifiers and differentiation of AD from normal based on T2-weighted MRI with shearlet transform (ST) and K-nearest neighbors (KNN) classifiers.


In this work, we proposed a methodology based on DWT with three-level decomposition feature extraction (Figure 1).


Based on statistics (mean, variance, skewness) of features and principal component analysis (PCA) for dimension reduction, we used five classifiers, including multi-layer perceptron neural network (MLPNN) (Figure 2), KNN, support vector machine (SVM) (Figure 3), and naïve Bayesian (NB) with the majority vote method to fuse them into one ensemble classifier.


The proposed methodology was evaluated using 100 T2-weighted MRI of AD and cognitive normal (CN) subjects, which were chosen from the Harvard Medical School website. The accuracy, specificity, and sensitivity achieved from our methodology were 95%, 90%, and 100%, respectively by using a 10-fold cross-validation strategy.


Our study showed that the stacking method for classification of AD and CN was better than using one classifier and comparable with state-of-the-art methods.

To see figures, please refer to the PDF file.

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