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Siamese Capsule Network (SNNCap): Cognitive Analysis for Alzheimer's Disease Classification From MRI Data.

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    This study introduces a novel Siamese Capsule Network (SNNCap) for Alzheimer's Disease (AD) detection using MRI scans. SNNCap enhances diagnostic accuracy by preserving spatial information and mimicking cognitive reasoning for improved classification of dementia severity.

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    Area of Science:

    • Neuroimaging
    • Artificial Intelligence
    • Medical Diagnostics

    Background:

    • Alzheimer's Disease (AD) detection is crucial for effective patient care and treatment.
    • Magnetic Resonance Imaging (MRI) provides high-resolution brain structure data vital for diagnosing neurological disorders.
    • Existing deep learning methods for AD detection using MRI, such as Siamese Convolutional Neural Networks (SCNN) with ResNet-34, face challenges in preserving spatial information due to pooling operations.

    Purpose of the Study:

    • To develop a cognitively inspired deep learning approach for accurate classification of Alzheimer's Disease severity from MRI scans.
    • To address limitations of previous methods in capturing spatial relationships and part-whole hierarchies within brain MRI data.
    • To improve the generalizability and accuracy of automated AD detection systems.

    Main Methods:

    • Proposed a Siamese Capsule Network (SNNCap) model for classifying MRI images into dementia severity categories: Non Demented, Very Mild Demented, Mild Demented, and Moderate Demented.
    • Utilized ResNet-18 for feature extraction and capsule layers within SNNCap to preserve crucial spatial and part-whole relationships.
    • Implemented a reference-based validation strategy where test images are compared against known examples, mimicking human cognitive reasoning.

    Main Results:

    • The SNNCap model demonstrated strong performance in classifying unseen MRI data.
    • The system effectively preserved spatial and part-whole relationships, overcoming limitations of pooling operations in traditional CNNs.
    • Classification reports and confusion matrices confirmed the model's effectiveness in differentiating dementia severity levels.

    Conclusions:

    • The proposed SNNCap offers a promising cognitively inspired approach for Alzheimer's Disease detection using MRI.
    • Preserving spatial and part-whole relationships through capsule networks enhances diagnostic accuracy and generalizability.
    • This method represents an advancement in automated analysis of neuroimaging data for neurological disorder diagnosis.