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Related Concept Videos

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Updated: Jun 4, 2025

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Classifying Alzheimer's Disease Using a Finite Basis Physics Neural Network.

Logeshwari Dhavamani1, Sagar Vasantrao Joshi2, Pavan Kumar Varma Kothapalli3

  • 1Department of Information Technology, St Joseph's Institute of Technology, Chennai, Tamil Nadu, India.

Microscopy Research and Technique
|December 20, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning method, CAD-FBPINN, for classifying Alzheimer's disease (AD) stages using MRI scans. The optimized approach significantly improves accuracy and precision in identifying cognitive impairments, offering a promising tool for early diagnosis.

Keywords:
Alzheimer's diseaseNewton‐time‐extracting wavelet transformfinite basis physics‐informed neural networksreverse lognormal Kalman filtersea‐horse optimization algorithm

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Alzheimer's disease (AD) is characterized by progressive neurodegeneration, leading to cognitive decline.
  • Accurate classification of AD stages using functional magnetic resonance imaging (fMRI) faces challenges in data quality, interpretability, and standardization.
  • Deep learning offers potential solutions for reliable AD classification from medical images.

Purpose of the Study:

  • To propose a novel deep learning model, Classifying AD using a finite basis physics neural network (CAD-FBPINN), for accurate AD classification.
  • To enhance fMRI image preprocessing and feature extraction for improved classification performance.
  • To optimize the CAD-FBPINN model using the sea-horse optimization algorithm (SHOA) for superior AD staging.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) data were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
  • Images underwent preprocessing using a reverse lognormal Kalman filter (RLKF) and feature extraction via Newton-time-extracting wavelet transform (NTEWT).
  • The extracted features were classified using a sea-horse optimization algorithm (SHOA)-optimized finite basis physics neural network (FBPINN).

Main Results:

  • The proposed CAD-FBPINN method demonstrated significant improvements in accuracy, precision, and negative predictive value (NPV) compared to existing methods.
  • Specifically, the method achieved higher accuracy (30.53%, 23.34%, 32.64%), precision (20.53%, 25.34%, 29.64%), and NPV (20.53%, 25.34%, 29.64%) over baseline approaches.
  • The CAD-FBPINN technique outperformed other methods like DC-AD-AlexNet and PDP-ADI-GCNN in classifying various AD stages.

Conclusions:

  • The CAD-FBPINN technique, optimized with SHOA, provides a robust and effective approach for classifying Alzheimer's disease stages using fMRI data.
  • The method addresses key challenges in AD classification, offering potential for trustworthy and practical therapeutic applications.
  • This deep learning approach shows promise for early detection and accurate staging of Alzheimer's disease.