An Ensemble of Classifiers based Approach for Prediction of Alzheimer's Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features

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  • Additional Information
    • Publication Information:
      Stefan cel Mare University of Suceava, 2018.
    • Publication Date:
      2018
    • Collection:
      LCC:Electrical engineering. Electronics. Nuclear engineering
      LCC:Computer engineering. Computer hardware
    • Abstract:
      Alzheimer's is a neurodegenerative disease caused by the destruction and death of brain neurons resulting in memory loss, impaired thinking ability, and in certain behavioral changes. Alzheimer disease is a major cause of dementia and eventually death all around the world. Early diagnosis of the disease is crucial which can help the victims to maintain their level of independence for comparatively longer time and live a best life possible. For early detection of Alzheimer's disease, we are proposing a novel approach based on fusion of multiple types of features including hemodynamic, volumetric and textural features of the brain. Our approach uses non-invasive fMRI with ensemble of classifiers, for the classification of the normal controls and the Alzheimer patients. For performance evaluation, ten-fold cross validation is used. Individual feature sets and fusion of features have been investigated with ensemble classifiers for successful classification of Alzheimer's patients from normal controls. It is observed that fusion of features resulted in improved results for accuracy, specificity and sensitivity.
    • File Description:
      electronic resource
    • ISSN:
      1582-7445
      1844-7600
    • Relation:
      https://doaj.org/toc/1582-7445; https://doaj.org/toc/1844-7600
    • Accession Number:
      10.4316/AECE.2018.01008
    • Rights:
      Journal Licence: CC BY-NC-ND
    • Accession Number:
      edsdoj.9c8b0d63b4654ad0a784ae5d9f8edc65
  • Citations
    • ABNT:
      MALIK, F.; FARHAN, S.; FAHIEM, M. A. An Ensemble of Classifiers based Approach for Prediction of Alzheimer’s Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features. Advances in Electrical and Computer Engineering, [s. l.], n. 1, p. 61, 2018. Disponível em: . Acesso em: 22 nov. 2019.
    • AMA:
      MALIK F, FARHAN S, FAHIEM MA. An Ensemble of Classifiers based Approach for Prediction of Alzheimer’s Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features. Advances in Electrical and Computer Engineering. 2018;(1):61. doi:10.4316/AECE.2018.01008.
    • APA:
      MALIK, F., FARHAN, S., & FAHIEM, M. A. (2018). An Ensemble of Classifiers based Approach for Prediction of Alzheimer’s Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features. Advances in Electrical and Computer Engineering, (1), 61. https://doi.org/10.4316/AECE.2018.01008
    • Chicago/Turabian: Author-Date:
      MALIK, F., S. FARHAN, and M. A. FAHIEM. 2018. “An Ensemble of Classifiers Based Approach for Prediction of Alzheimer’s Disease Using FMRI Images Based on Fusion of Volumetric, Textural and Hemodynamic Features.” Advances in Electrical and Computer Engineering, no. 1: 61. doi:10.4316/AECE.2018.01008.
    • Harvard:
      MALIK, F., FARHAN, S. and FAHIEM, M. A. (2018) ‘An Ensemble of Classifiers based Approach for Prediction of Alzheimer’s Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features’, Advances in Electrical and Computer Engineering, (1), p. 61. doi: 10.4316/AECE.2018.01008.
    • Harvard: Australian:
      MALIK, F, FARHAN, S & FAHIEM, MA 2018, ‘An Ensemble of Classifiers based Approach for Prediction of Alzheimer’s Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features’, Advances in Electrical and Computer Engineering, no. 1, p. 61, viewed 22 November 2019, .
    • MLA:
      MALIK, F., et al. “An Ensemble of Classifiers Based Approach for Prediction of Alzheimer’s Disease Using FMRI Images Based on Fusion of Volumetric, Textural and Hemodynamic Features.” Advances in Electrical and Computer Engineering, no. 1, 2018, p. 61. EBSCOhost, doi:10.4316/AECE.2018.01008.
    • Chicago/Turabian: Humanities:
      MALIK, F., S. FARHAN, and M. A. FAHIEM. “An Ensemble of Classifiers Based Approach for Prediction of Alzheimer’s Disease Using FMRI Images Based on Fusion of Volumetric, Textural and Hemodynamic Features.” Advances in Electrical and Computer Engineering, no. 1 (2018): 61. doi:10.4316/AECE.2018.01008.
    • Vancouver/ICMJE:
      MALIK F, FARHAN S, FAHIEM MA. An Ensemble of Classifiers based Approach for Prediction of Alzheimer’s Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features. Advances in Electrical and Computer Engineering [Internet]. 2018 [cited 2019 Nov 22];(1):61. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsdoj&AN=edsdoj.9c8b0d63b4654ad0a784ae5d9f8edc65&custid=s8280428