Classification of Alzheimer Disease based on Normalized Hu Moment Invariants and Multiclassifier
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 11
Abstract
There is a great benefit of Alzheimer disease (AD) classification for health care application. AD is the most common form of dementia. This paper presents a new methodology of invariant interest point descriptor for Alzheimer disease classification. The descriptor depends on the normalized Hu Moment Invariants (NHMI). The proposed approach deals with raw Magnetic Resonance Imaging (MRI) of Alzheimer disease. Seven Hu moments are computed for extracting images’ features. These moments are then normalized giving new more powerful features that highly improve the classification system performance. The moments are invariant which is the robustness point of Hu moments algorithm to extract features. The classification process is implemented using two different classifiers, K-Nearest Neighbors algorithm (KNN) and Linear Support Vector Machines (SVM). A comparison among their performances is investigated. The results are evaluated on Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The best classification accuracy is 91.4% for KNN classifier and 100% for SVM classifier.
Authors and Affiliations
Arwa Mohammed Taqi, Fadwa Al-Azzo, Mariofanna Milanova
Keywords
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- EP ID EP240414
- DOI 10.14569/IJACSA.2017.081102
- Views 109
- Downloads 0
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