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Multi-modality MRI for Alzheimer’s disease detection using deep learning

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Abstract

Diffusion tensor imaging (DTI) is a new technology in magnetic resonance imaging, which allows us to observe the insightful structure of the human body in vivo and non-invasively. It identifies the microstructure of white matter (WM) connectivity by estimating the movement of water molecules at each voxel. This makes possible the identification of the damage to WM integrity caused by Alzheimer’s disease (AD) at its early stage, called mild cognitive impairment (MCI). Furthermore, the brain’s gray matter (GM) atrophy characterizes the main structural changes in AD, which can be sensitively detected by structural MRI (sMRI) modality. In this research, we aimed to classify the Alzheimer’s diseases stages by developing a novel multi-modality MRI (DTI and sMRI) fusion strategy to detect WM alterations and GM atrophy in AD patients. The latter is based on a 2-dimensional deep convolutional neural network (CNN) features extractor and a support vector machine (SVM) classifier. The fusion framework consists of merging features extracted from DTI scalar metrics [(fractional anisotropy (FA) and mean diffusivity (MD)], and GM using 2D-CNN and feeding them to SVM to classify AD versus cognitively normal (CN), AD versus MCI, and MCI versus CN. Our novel multimodal AD method demonstrates a superior performance with an accuracy of 99.79%, 99.6%, and 97.00% for AD/CN, AD/MCI, and MCI/CN respectively.

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Data availability

The data used in this study were obtained from the Alzheimer’s disease neuroimaging initiative (ADNI). The full data can be downloaded from (http://adni.loni.usc.edu).

Code availability

The code that supported the fndings of this study is available on request from the corresponding author (Latifa Houria).

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Acknowledgements

This project was developped by the LASICOM laboratory of University of Blida 1, Department of Electrical Engineering. The authors would like to acknowledge the Alzheimer’s Disease Neuroimaging Initiative (ADNI) for the public sharing of these precious neuroimaging data (http://adni.loni.usc.edu). We are thankful to the General Directorate of Scientific Research and Technological Development (DGRSDT) for their support in developing this work.

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  1. All authors have contributed equally to this work.

Authors and Affiliations

  1. LASICOM Laboratory, Department of Electronics, Faculty of Technology, University of Blida 1, Blida, Algeria

    Latifa Houria, Noureddine Belkhamsa, Assia Cherfa & Yazid Cherfa

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  1. Latifa Houria
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  2. Noureddine Belkhamsa
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  3. Assia Cherfa
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Correspondence to Latifa Houria.

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Houria, L., Belkhamsa, N., Cherfa, A. et al. Multi-modality MRI for Alzheimer’s disease detection using deep learning. Phys Eng Sci Med 45, 1043–1053 (2022). https://doi.org/10.1007/s13246-022-01165-9

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  • DOI: https://doi.org/10.1007/s13246-022-01165-9

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