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Related Experiment Video

Updated: Jan 17, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Multi-filter stacking in inception V3 for enhanced Alzheimer's severity classification.

Ateeqa Iqbal1, Khalid Iqbal2, Yaser Ali Shah1

  • 1Department of Computer Science, COMSATS University, Islamabad, Attock Campus, Pakistan.

Neuroscience
|September 18, 2025
PubMed
Summary

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This study introduces CASFI, a new AI method for classifying Alzheimer's disease severity using MRI scans. CASFI achieves 97.27% accuracy, improving early diagnosis and patient care.

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Alzheimer's disease (AD) causes brain volume decline and neuronal loss, impacting memory.
  • Automated AD classification is difficult due to patient variability and overlapping features.
  • Existing machine learning models like SVMs and DNNs require improvement for accuracy and efficiency.

Purpose of the Study:

  • To develop a novel, accurate, and robust method for classifying Alzheimer's disease severity.
  • To enhance early diagnosis and clinical decision-making for Alzheimer's disease management.

Main Methods:

  • Proposed CASFI (Classifying Alzheimer's Severity using Filter Integration), integrating Multi-Filter Stacking with Inception V3 architecture.
  • Utilized diverse convolutional filter sizes to capture multiscale spatial features from MRI data.
Keywords:
Alzheimer’s diseaseClassificationConvolutional Neural NetworkDeep learningDementiaInceptionV3Stacking

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  • Leveraged CASFI to detect subtle structural variations indicative of different Alzheimer's disease stages.

    • Main Results:

    • CASFI achieved a high accuracy of 97.27% in classifying Alzheimer's disease severity from MRI data.
    • The proposed method demonstrated superior performance compared to baseline deep learning and traditional classifiers.
    • CASFI showed enhanced robustness in differentiating between disease stages.

    Conclusions:

    • CASFI offers a significant advancement in automated Alzheimer's disease classification.
    • The approach supports more accurate early diagnosis and informed clinical decision-making.
    • CASFI provides a valuable tool for healthcare professionals in managing Alzheimer's disease.