Classification of some dementia types due to feature selection with artificial neural networks

Brains of patients with dementia show physical differences according to disease types and phases. Physical characteristics of brains such as cortical thickness and volumes of some parts have a significant effect on determining the type of the disease. Magnetic resonance imaging devices create visual...

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Bibliographic Details
Published in2016 24th Signal Processing and Communication Application Conference (SIU) pp. 1121 - 1124
Main Authors Okyay, Savas, Adar, Nihat, Ozkan, Kemal, Saylisoy, Suzan, Adapinar, Belgin Demet Ozbabalik, Adapinar, Baki
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2016
Subjects
Alzheimer's disease
Artificial neural networks
classification
Dementia
dicom
Feature extraction
Freesurfer
Magnetic resonance imaging
Neuroimaging
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Summary:Brains of patients with dementia show physical differences according to disease types and phases. Physical characteristics of brains such as cortical thickness and volumes of some parts have a significant effect on determining the type of the disease. Magnetic resonance imaging devices create visual files which contain patient information appropriate to the medical imaging standards. Using image processing techniques, numerical expressions of patients' brains can be extracted via these files. By means of using these numeric values with classification methods, patients can be classified. In this study, samples having three diseases: Alzheimer's disease, vascular dementia and fron to temporal dementia are used. After extracting cortical surface area, thickness and volume features, samples are classified successfully with artificial neural networks due to feature selection.
DOI:10.1109/SIU.2016.7495941