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    Area of Science:

    • Neuroimaging
    • Artificial Intelligence
    • Biomedical Engineering

    Background:

    • Alzheimer's disease (AD) presents a growing global health challenge.
    • Early identification of AD and mild cognitive impairment (MCI) is difficult but crucial for timely intervention.
    • Neuroimaging techniques like MRI and PET reveal structural and metabolic brain changes indicative of AD years before symptom onset.

    Purpose of the Study:

    • To propose a novel image fusion approach for enhanced Alzheimer's disease detection.
    • To integrate structural (MRI) and metabolic (PET) neuroimaging data using advanced machine learning.
    • To improve the classification accuracy of Alzheimer's disease (AD) and mild cognitive impairment (MCI) using a fusion-based ensemble model.

    Main Methods:

    • A wavelet packet transform-based fusion technique was developed for MRI and PET scans.
    • An eight-layer Convolutional Neural Network (CNN) extracted multi-layer features from fused images.
    • Features were processed by an ensemble of Random Vector Functional Link (RVFL) networks with s-membership fuzzy activation functions for outlier robustness.

    Main Results:

    • The proposed fusion approach demonstrated effective feature extraction and classification.
    • The ensemble RVFL model achieved notable performance in distinguishing between healthy controls (CN), Alzheimer's disease (AD), and mild cognitive impairment (MCI).
    • Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset validated the model's effectiveness.

    Conclusions:

    • The developed wavelet packet transform-based fusion method shows promise for early AD and MCI detection.
    • Ensemble learning with RVFL networks offers a robust approach for analyzing complex neuroimaging data.
    • This fusion-based strategy enhances diagnostic capabilities for neurodegenerative diseases.