Feature Selection from 3D Brain Model for Some Dementia Subtypes Using Genetic Algorithm
AbstractBrain scans that are appropriate to the medical standards are obtained from magnetic resonance imaging devices. Through image processing techniques, 3D brain models can be constructed by mapping medical brain imaging files structurally. Physical characteristics of patient brains can be extracted from those 3D brain models. Characteristics of some specific brain regions are more efficacious in predicting the type of the disease. For that reason, researches are made for finding the worthwhile features out using cortical volumes, gray volumes, surface areas, and thickness averages for left and right brain parts separately or together. The main objective of this work is determining more influential sections throughout the entire brain in establishing the clinical diagnosis. To that end, among all the measurements exported from 3D models, the significant brain features that are effective in identifying some dementia subtypes are sought. The dataset has 3D brain models generated from magnetic resonance scans of 63 samples. Each sample is labeled with one of the following three disease types: Alzheimer’s disease (19), frontotemporal dementia (19), and vascular dementia (25). The genetic algorithm based wrapper feature selection method with various classifiers is proposed to select the features that state the aforementioned dementia subtypes best. The tests are performed by applying cross validation technique and confusion matrices are shown. At the end, the best features are listed, and the accuracy results up to 95.2% are achieved.
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