Title: A CNN and spatial tract-based statistics-based approach for the diagnosis of Alzheimer's disease

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: The prevalence of Alzheimer's disease (AD) is growing dramatically each year, making it critical to find efficient strategies to detect the disease early on and prevent its progression. In this work, we present an approach combining tract-based spatial statistics (TBSS) and convolutional neural network (CNN) to classify the AD and mild cognitive impairment (MCI) from a cognitively normal (CN) subject using the diffusion tensor imaging (DTI). The TBSS was applied to generate the WM skeleton from the two DTI maps fractional anisotropy (FA) and mode of anisotropy (MO). The CNN is trained first on FA-Skeleton and MO-Skeleton and then fine-tuning on FA and MO relevant slices. This combinatory procedure achieved a higher result and represented a powerful diagnostic tool.

Keywords: diffusion tensor imaging; DTI; Alzheimer's disease; convolutional neural network; CNN; tract-based spatial statistics; TBSS; fractional anisotropy; mode of anisotropy.

DOI: 10.1504/IJMEI.2024.140809

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.5, pp.493 - 501

Received: 07 Feb 2022
Accepted: 06 Jun 2022
Published online: 03 Sep 2024 *

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