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    Area of Science:

    • Neuroscience
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Alzheimer's disease (AD) is a leading cause of dementia, characterized by cognitive decline.
    • Early detection, often through identifying mild cognitive impairment (MCI), is crucial for managing AD.
    • Standard diagnostic biomarkers include structural changes (MRI) and metabolic abnormalities (PET).

    Purpose of the Study:

    • To propose a novel method for early Alzheimer's disease detection.
    • To integrate structural and metabolic information from MRI and PET scans.
    • To enhance diagnostic accuracy for neurodegenerative diseases.

    Main Methods:

    • Wavelet transform-based multimodality fusion of MRI and PET scans.
    • Feature extraction using the ResNet-50 deep learning model.
    • Classification of features using an optimized Random Vector Functional Link (RVFL) network with an evolutionary algorithm.

    Main Results:

    • The proposed method demonstrates efficacy in early Alzheimer's disease detection.
    • Multimodality fusion effectively incorporates structural and metabolic information.
    • Optimized RVFL network achieves high accuracy in classification tasks.

    Conclusions:

    • The developed technique shows promise for the early diagnosis of Alzheimer's disease.
    • Combining MRI and PET data through wavelet fusion enhances diagnostic capabilities.
    • Deep learning and evolutionary algorithms offer powerful tools for neurodegenerative disease research.