Functional magnetic resonance imaging classification based on random forest algorithm in Alzheimer's disease

Yu Wang; Changsheng Li

doi:10.1117/12.2538059

27 November 2019 Functional magnetic resonance imaging classification based on random forest algorithm in Alzheimer's disease

Yu Wang, Changsheng Li

Author Affiliations +

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 1132104 (2019) https://doi.org/10.1117/12.2538059
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

Abstract

For classifying Alzheimer's disease (AD) by analyzing medical image data, in this paper a computer-aided diagnosis method is proposed based on random forest algorithm. In this study functional magnetic resonance imaging (fMRI) data including 34 AD patients, 35 mild cognitive impairments (MCI) and 35 normal controls (NC) is collected. Firstly, functional connection between the different regions of whole brain is calculated using Pearson correlation coefficient. Then the importance of the functional connection between different brain regions is measured and the important features are selected using the random forest algorithm. Finally, classification is performed using support vector machine (SVM) classifier with ten-fold cross-validation. The classification model based on random forest and SVM has a good effect on the recognition of AD, and the classification accuracy rate can reach 90.68%. Functional connection characteristics can be effectively analyzed by the random forest algorithm which can distinguish AD, MCI and NC accurately. At the same time, the abnormal brain regions of AD pathogenesis can be obtained. The related experimental results can provide an objective reference for the early clinical diagnosis of AD.

Citation Download Citation

Yu Wang and Changsheng Li "Functional magnetic resonance imaging classification based on random forest algorithm in Alzheimer's disease", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 1132104 (27 November 2019); https://doi.org/10.1117/12.2538059

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KEYWORDS

Brain

Functional magnetic resonance imaging

Feature selection

Alzheimer's disease

Neuroimaging

Feature extraction

Data modeling

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