MRI brain pattern analysis for detection of Alzheimer's disease using random forest classifier

Abstract

Alzheimer's disease (AD) is the most common type of dementia and a major cause of disability worldwide.It is a progressive and degenerative disease that affects brain cells and its early diagnosis has been essential for appropriate intervention by health professionals. In this work, Random forest classifier is used to identify Alzheimer's disease (AD) affected patient using brain magnetic resonance imaging (MRI). Random Forest grows many classification trees. To classify a new input vector, each tree votes for a class and the forest chooses the class having the majority of votes over all the trees in the forest. A set of MR brain images from healthy and pathological subjects extracted from the OASIS (Open Access Series of Imaging Studies) database are used to evaluate the performance of the proposed method. Nonsubsampled Contourlet Transform decomposes the input image into many sub bands. Features are extracted from the input images and also from the transformed images. In this work, two groups of features are used to extract the information. In the first groups, features such as intensity, orientation, and edge information & in the second group, features such as color Mean, Symmetry, Center Black area, Total Brain Area, Mean, Variance, Skewness, Image Kurtosis, Gradient Mean and Gradient Variance are calculated. The features are given as the input to Random forest classifier and also to the Support Vector Machine (SVM) for comparing the performance of proposed work with the well-known existing SVM classifier. From the classification results, it is observed that Random forest classifier performed better than the SVM classifier and other existing work.