Journal
JOURNAL OF ALZHEIMERS DISEASE
Volume 72, Issue 2, Pages 515-524Publisher
IOS PRESS
DOI: 10.3233/JAD-190704
Keywords
Alzheimer's disease; computer vision; feature extraction; individual grey matter; machine learning; magnetic resonance imaging
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Funding
- Estonian Centre of Excellence in IT (EXCITE) - European Regional Development Fund
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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.
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