Title: Diffusion tensor imaging for Alzheimer's disease classification using a bag of features and majority voting

Authors: Latifa Houria; Noureddine Belkhamsa; Assia Cherfa; Yazid Cherfa

Addresses: LASICOM Laboratory, Department of Electronical Engineering, University of Blida 1, Blida, Algeria ' LASICOM Laboratory, Department of Electronical Engineering, University of Blida 1, Blida, Algeria ' LASICOM Laboratory, Department of Electronical Engineering, University of Blida 1, Blida, Algeria ' LASICOM Laboratory, Department of Electronical Engineering, University of Blida 1, Blida, Algeria

Abstract: Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia. Thus, various neuroimaging-based methods were proposed to detect this disease at its early stage, called mild cognitive impairment (MCI). We developed a novel approach combining Diffusion tensor imaging (DTI)-indices and ensemble learning to classify AD. A bag of features (BoF) is used to retrieve the locale features, and a support vector machine (SVM) is applied for classification. The majority voting technique is used to combine the final predicted labels. The proposed method achieves an accuracy of 94.0%, 97.0%, and 95.9% to classify CN vs. MCI, CN vs. AD, and MCI vs. AD, respectively.

Keywords: diffusion tensor imaging; Alzheimer's disease; bag of features; BoF; support vector machine; SVM; majority voting.

DOI: 10.1504/IJMEI.2025.149544

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.6, pp.547 - 555

Received: 04 Jun 2022
Accepted: 10 Feb 2023
Published online: 07 Nov 2025 *

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