Abstract

Info

Title:: Medical image classification for Alzheimers disease diagnosis based on random forest algorithm

Author(s):: LI Changsheng; WANG Yu; XIAO Hongbing; XING Suxia; School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China

Keywords:: Keywords: Alzheimers disease functional magnetic resonance imaging random forest feature selection

Abstract:: Abstract: For accurately classifying the medical images of Alzheimers disease (AD) and assisting the doctors in making an accurate diagnosis of the patients condition, a computer-aided diagnosis method is proposed based on random forest algorithm. The functional magnetic resonance imaging (fMRI) data of 34 AD patients, 35 patients with mild cognitive impairment (MCI) and 35 normal controls are collected for feature extraction and classification. Firstly, the functional connections between different brain regions are calculated using Pearson correlation coefficient. Then the importance of the functional connections between different brain regions is assessed and important features are selected by random forest algorithm. Finally, support vector machine classifier is used for classification, and ten-fold cross-validation for estimating the classification accuracy. The experimental results show that random forest algorithm can be use to effectively analyze the functional connection characteristics and obtain the abnormal brain regions of AD pathogenesis. The classification model based on random forest and support vector machine has a good effect on the recognition of AD and MCI, with a classification accuracy of 90.68%. The related experimental results provide an objective reference for the early clinical diagnosis of AD.