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Volumetric Histogram-Based Alzheimer's Disease Detection Using Support Vector Machine

Date

2019

Author

Elshatoury, Heba
Avots, Egils
Anbarjafari, Gholamreza

Metadata

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Citation

Elshatoury, H., Avots, E., Anbarjafari, G., & Alzheimer’s Disease Neuroimaging Initiative. (January 01, 2019). Volumetric Histogram-Based Alzheimer's Disease Detection Using Support Vector Machine. Journal of Alzheimer's Disease : Jad, 72, 2, 515-524.

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.

Source

Journal of Alzheimer's disease : JAD

Volume

72

Issue

2

URI

https://doi.org/10.3233/JAD-190704
https://hdl.handle.net/20.500.11782/935

Collections

  • MF - EEM Makale Koleksiyonu [87]
  • Scopus İndeksli Yayınlar Koleksiyonu [577]
  • WoS İndeksli Yayınlar Koleksiyonu [517]



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