Abstract
Mild cognitive impairment (MCI) is considered as a transitional stage between normal aging and dementia. MCI has a high risk to convert into Alzheimer’s disease (AD). In the related research, the volumetric analysis of hippocampus is the most extensive study. However, the segmentation and identification of the hippocampus are highly complicated and time-consuming. Therefore, we designed a MRI-based classification framework to distinguish the patients of MCI and AD from normal individuals. First, volumetric features and shape features were extracted from MRI data. Afterward, Principle component analysis (PCA) was utilized to decrease the dimensions of feature space. Finally, a Self-organizing map classifier was trained for patient classification. By combining the volumetric features and shape features, the classification accuracy is reached to 86.76%, 66.67%, and 46.67% in AD, amnestic MCI (aMCI), and dysexecutive MCI (dMCI), respectively. In addition, with the help of PCA process, the classification result is improved to 93.63%, 73.33%, and 53.33% in AD, aMCI and dMCI, respectively.
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Yang, ST., Lee, JD., Huang, CH., Wang, JJ., Hsu, WC., Wai, YY. (2010). An Image-Aided Diagnosis System for Dementia Classification Based on Multiple Features and Self-Organizing Map. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_57
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DOI: https://doi.org/10.1007/978-3-642-17534-3_57
Publisher Name: Springer, Berlin, Heidelberg
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