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Comparative efficacy of histogram-based local descriptors and CNNs in the MRI-based multidimensional feature space for the differential diagnosis of Alzheimer’s disease: a computational neuroimaging approach

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Abstract

The utilisation of magnetic resonance imaging (MRI) images for the automated detection of Alzheimer’s disease has garnered significant attention in recent years. This interest stems from the progress made in machine learning techniques and the possible application of such methods in the field of diagnostics. This study aims to evaluate the performance of 16 histogram-based image texture descriptors and features extracted from 18 pre-trained convolutional neural networks in characterising brain patterns observed in 2D slices of MRI images. The primary objective is to determine the most effective feature types for this task. The characteristics were taken from the magnetic resonance imaging (MRI) dataset given by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The study involved the calculation of features on 2D axial, coronal, and sagittal slices, followed by classification using five binary machine learning algorithms. The objective was to differentiate between individuals with normal cognitive function and those diagnosed with Alzheimer’s disease. The proposed methodology additionally facilitated the identification of specific brain areas to be selected for each axis, in order to achieve optimal accuracy. This involved determining the matching feature and classifier combinations.

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The ADNI dataset can be obtained by going through the application process, which includes acceptance of the Data Use Agreement and submission of an online application form that can be found at https://adni.loni.usc.edu/.

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Acknowledgements

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organisation is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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Authors and Affiliations

  1. iCV Research Lab, Institute of Technology, University of Tartu, Tartu, Estonia

    Egils Avots, Akbar A. Jafari, Cagri Ozcinar & Gholamreza Anbarjafari

  2. iVCV OÜ, Tartu, Estonia

    Gholamreza Anbarjafari

  3. Faculty of Engineering, Yildiz Technical University, Istanbul, Turkey

    Gholamreza Anbarjafari

  4. PwC Advisory, Helsinki, Finland

    Gholamreza Anbarjafari

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Contributions

E. Avots developed the methodology and conducted experiments. A. A. Jafari worked on data preprocessing and conducted experiments. E. Avots, A. A. Jafari, C. Ozcinar wrote the main manuscript text. C. Ozcinar was the main article editor. G. Anbarjafari guided the research process. Alzheimer’s Disease Neuroimaging Initiative (ADNI) provided the MRI data and ADNI Data and Publications Committee (DPC) conducted preliminary revision. All authors reviewed the manuscript.

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ADNI conducts research according to Good Clinical Practice guidelines, US 21CFR Part 50- Protection of Human Subjects, and Part 56 - Institutional Review Boards (IRBs) / Research Ethics Boards (REBs), and pursuant to state and federal HIPAA regulations.

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Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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Avots, E., Jafari, A.A., Ozcinar, C. et al. Comparative efficacy of histogram-based local descriptors and CNNs in the MRI-based multidimensional feature space for the differential diagnosis of Alzheimer’s disease: a computational neuroimaging approach. SIViP 18, 2709–2721 (2024). https://doi.org/10.1007/s11760-023-02942-z

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