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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Classification of Alzheimer's Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional

Monika Sethi1, Sachin Ahuja1, Shalli Rani1

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India.

Computational and Mathematical Methods in Medicine
|October 14, 2021
PubMed
Summary

Machine learning models, specifically deep learning with Bayesian optimization, significantly improve early Alzheimer's disease diagnosis from MRI scans. This approach enhances accuracy and reduces the need for extensive manual tuning.

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Alzheimer's disease (AD) diagnosis is challenging in early stages using traditional methods.
  • Machine learning (ML) offers promising solutions for early AD detection.
  • Data heterogeneity in AD complicates diagnostic accuracy.

Purpose of the Study:

  • To develop optimized deep learning models for early Alzheimer's disease (AD) detection.
  • To enhance the accuracy of AD classification using magnetic resonance imaging (MRI) scans.
  • To investigate the efficacy of Bayesian optimization for tuning deep learning hyperparameters.

Main Methods:

  • Proposed four 2D and 3D Convolutional Neural Network (CNN) frameworks.
  • Utilized Bayesian optimization for hyperparameter tuning (learning rate, optimizers, hidden units).
  • Integrated Long Short-Term Memory (LSTM) with data augmentation for model improvement.

Main Results:

  • Achieved relative improvements of 7.03% to 11.99% over manually tuned models.
  • Demonstrated superior model settings with fewer iterations using Bayesian optimization.
  • Successfully performed binary and ternary classification of early AD on MRI scans.

Conclusions:

  • Bayesian optimization effectively reduces experimental iterations for deep learning model development.
  • The proposed deep learning models show significant potential for accurate early Alzheimer's disease diagnosis.
  • This ML-driven approach offers a more efficient and reliable alternative to manual diagnostic methods.