Affiliations: [a] iCV Research Lab, Institute of Technology, University of Tartu, Tartu, Estonia
| [b] Department of Electrical and Electronic Engineering, Hasan Kalyoncu University, Gaziantep, Turkey
Correspondence:
[*]
Correspondence to: Prof. Gholamreza Anbarjafari, iCV Research Lab, Institute of Technology, University of Tartu, Tartu 50411, Estonia. Tel.: +372 737 4855; E-mail: shb@ut.ee.
Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Abstract: In this research work, machine learning techniques are used to classify magnetic resonance imaging brain scans of people with Alzheimer’s disease. This work deals with binary classification between Alzheimer’s disease and cognitively normal. Supervised learning algorithms were used to train classifiers in which the accuracies are being compared. The database used is from The Alzheimer’s Disease Neuroimaging Initiative (ADNI). Histogram is used for all slices of all images. Based on the highest performance, specific slices were selected for further examination. Majority voting and weighted voting is applied in which the accuracy is calculated and the best result is 69.5% for majority voting.
