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Related Experiment Video

Updated: Jan 18, 2026

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Attention Gated-VGG with deep learning-based features for Alzheimer's disease classification.

Deepthi K Moorthy1, P Nagaraj2

  • 1Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Srivilliputhur, Tamil Nadu, India.

Neurodegenerative Disease Management
|September 10, 2025
PubMed
Summary

Early detection of Alzheimer's disease (AD) is crucial. An Attention Gated-VGG deep learning model achieved 96.7% accuracy in classifying AD from brain images, showing promise for early diagnosis.

Keywords:
Alzheimer’s classificationDeep learningMRIdisease detectionoptimization algorithm

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Alzheimer's disease (AD) is a leading neurodegenerative disorder causing cognitive decline and dementia.
  • Early detection of AD is critical for timely intervention and management.
  • Current diagnostic methods require improvement for earlier and more accurate identification.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model for the accurate classification of Alzheimer's disease.
  • To leverage advanced image processing and feature extraction techniques for improved AD detection.
  • To assess the efficacy of the Attention Gated-VGG model in differentiating AD patients from controls.

Main Methods:

  • Image pre-processing including resizing and median filtering.
  • Data augmentation to enhance the training dataset.
  • Feature extraction using a Whale Optimization Algorithm (WOA)-based ResNet and Convolutional Neural Network (CNN).
  • Classification using the proposed Attention Gated-VGG deep learning model.

Main Results:

  • The Attention Gated-VGG model achieved high classification performance.
  • Achieved an accuracy of 96.7%, sensitivity of 97.8%, and specificity of 96.3%.
  • Outperformed conventional methodologies in AD classification tasks.

Conclusions:

  • The Attention Gated-VGG model demonstrates significant potential as a tool for early Alzheimer's disease classification.
  • The proposed deep learning approach offers a promising avenue for improving diagnostic accuracy in neurodegenerative diseases.
  • This technique could aid clinicians in making earlier and more informed decisions regarding AD patient care.