An image-aided diagnosis system for dementia classification based on multiple features and self-organizing map

Authors:
Shih-Ting Yang;Jiann-Der Lee;Chung-Hsien Huang;Jiun-Jie Wang;Wen-Chuin Hsu;Yau-Yau Wai
Affiliations:
Department of Electrical Engineering, Chang Gung University, Taiwan;Department of Electrical Engineering, Chang Gung University, Taiwan;Department of Electrical Engineering, Chang Gung University, Taiwan;Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taiwan;Department of Neuroscience, Chang Gung Memorial Hospital, Taiwan;Department of Neuroscience, Chang Gung Memorial Hospital, Taiwan
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

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Self-Organizing Maps

Self-Organizing Maps
Combination of Multiple Features in Support Vector Machine with Principal Component Analysis in Application for Alzheimer's Disease Diagnosis

ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II

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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.