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Alzheimer's Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an

Raheleh Ghadami1, Javad Rahebi2

  • 1Department of Computer Engineering, Istanbul Topkapi University, 34662 Istanbul, Türkiye.

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Summary
This summary is machine-generated.

This study introduces an advanced machine learning (ML) and deep learning (DL) approach for accurate Alzheimer's disease diagnosis using MRI scans. The integrated method significantly improves classification accuracy, aiding early detection and intervention.

Keywords:
Alzheimer’s diseaseHarris Hawks Optimization (HHO) algorithmLSTM neural networkconvolutional neural network (CNN)magnetic resonance images (MRI)

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline.
  • Early diagnosis via MRI is crucial for effective medical intervention.
  • Current ML/DL methods face challenges in accurately differentiating AD from healthy states in MRI.

Purpose of the Study:

  • To develop an integrated ML/DL approach combined with swarm intelligence for enhanced AD classification.
  • To improve the accuracy of distinguishing between healthy and AD-affected individuals using MRI data.

Main Methods:

  • Feature extraction from AD-related MRI images using Convolutional Neural Networks (CNNs) and Gray Level Co-occurrence Matrix (GLCM).
  • Feature selection employing the Harris Hawks Optimization (HHO) algorithm.
  • Classification using Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks on the ADNI dataset.

Main Results:

  • The proposed integrated method achieved a high classification accuracy of 97.59%.
  • Achieved excellent sensitivity (97.41%) and precision (97.25%) in AD diagnosis.
  • Outperformed established models like VGG16, GLCM, and ResNet-50 in diagnostic performance.

Conclusions:

  • The integrated swarm intelligence, ML, and DL approach demonstrates significant efficacy in improving AD diagnosis.
  • Advanced feature extraction and selection techniques are key to enhancing diagnostic accuracy in medical imaging.
  • This study highlights the potential of integrated AI methods for advancing diagnostic tools in neuroimaging.