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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Detection of Neuritic Plaques in Alzheimer's Disease Mouse Model
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Detection and Classification of Alzheimer's Disease Using Deep and Machine Learning.

Muhammad Zaeem Khalid1, Nida Iqbal1, Babar Ali2

  • 1Department of Biomedical Engineering, University of Engineering and Technology Lahore, New Campus, Lahore 54000, Pakistan.

Tomography (Ann Arbor, Mich.)
|January 27, 2026
PubMed
Summary
This summary is machine-generated.

This study developed an AI framework combining clinical data and MRI scans for accurate Alzheimer's disease (AD) staging. Machine learning models achieved high accuracy, aiding timely diagnosis and personalized treatment.

Keywords:
Alzheimer’s diseaseGrad-CAM SHAPclinical datasetsdeep learningexplainability AImachine learningmagnetic resonance imaging

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Alzheimer's disease (AD) is the primary cause of dementia, presenting significant challenges in early diagnosis and management.
  • Traditional diagnostic methods for AD often lack sensitivity, especially in early disease stages.
  • A robust diagnostic approach is critical for improving patient outcomes and reducing socioeconomic impact.

Purpose of the Study:

  • To develop and validate a dual-modal framework integrating clinical data and MRI for enhanced Alzheimer's disease staging.
  • To leverage machine learning (ML) and deep learning (DL) models, augmented by explainable AI (XAI), for improved diagnostic accuracy.
  • To provide clinicians with a reliable tool for timely AD diagnosis and personalized treatment planning.

Main Methods:

  • Four ML classifiers (KNN, SVM, DT, RF) were trained on demographic and clinical features.
  • Five DL models (CNN, EfficientNetB3, DenseNet-121, ResNet-50, MobileNetV2) were applied to MRI scans for stage-wise classification.
  • SHAP and Grad-CAM techniques were employed for model interpretability and identification of key diagnostic features.

Main Results:

  • The Random Forest model achieved 97% accuracy using clinical data.
  • The CNN model demonstrated the highest performance (94%) in MRI-based AD staging.
  • Explainable AI identified significant indicators such as hippocampal atrophy and ventricular enlargement.

Conclusions:

  • Integrating clinical data, MRI, and interpretable AI significantly enhances the accuracy and reliability of Alzheimer's disease staging.
  • The proposed framework offers a clear and validated diagnostic pathway for clinicians.
  • This approach supports timely diagnosis and facilitates the adjustment of individualized treatment strategies for AD patients.