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- W2587851680 abstract "This paper proposes to use multilevel ROI-based features and machine learning method to improve the accuracy of qualitative recognition of mild cognitive disorder in parkinsonism. 77 Parkinson's patients and 32 normal controls with neuropsychological assessments and structural magnetic resonance images from the Parkinson's Progression Markers Initiative dataset are tested. Specifically, the BrainLab software is used to process images and measure volume of gray matter, thickness of the cortex, and surface area of the cortex at each region of interest (ROI). We utilize t-test, support vector machine (SVM), and minimum redundancy and maximum relevance (mRMR) methods conjunctively to select features and get the optimal features and the classifier. The experimental results reveal that our method with multilevel ROI-based features gives significant improvement of the classification performance compared with other methods using single-level ROI-based features (i.e., using only volume of gray matter, thickness of cortex, or surface area of cortex)." @default.
- W2587851680 created "2017-02-24" @default.
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- W2587851680 date "2016-10-01" @default.
- W2587851680 modified "2023-10-06" @default.
- W2587851680 title "Computer aided analysis of cognitive disorder in patients with Parkinsonism using machine learning method with multilevel ROI-based features" @default.
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- W2587851680 doi "https://doi.org/10.1109/cisp-bmei.2016.7853008" @default.
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