@inproceedings{a89316744c294dec881b27ea925f0d97,
title = "Non-negative matrix factorization in texture feature for classification of dementia with MRI data",
abstract = "This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Na{\"i}ve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).",
keywords = "classification, dementia, features selection, Non-negative matrix factorization",
author = "D. Sarwinda and Alhadi B. and G. Ardaneswari",
year = "2017",
month = jul,
day = "10",
doi = "10.1063/1.4991252",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
editor = "Sugeng, {Kiki Ariyanti} and Djoko Triyono and Terry Mart",
booktitle = "International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016",
note = "2nd International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016 ; Conference date: 01-11-2016 Through 02-11-2016",
}