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Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

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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.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
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Alzheimer's Disease: Treatment01:22

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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Related Experiment Video

Updated: Jul 15, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and

Irshad Ahmad1, Muhammad Hameed Siddiqi2, Sultan Fahad Alhujaili3

  • 1Department of Computer Science, Islamia College, Peshawar 25000, KPK, Pakistan.

Healthcare (Basel, Switzerland)
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid machine learning approach for Alzheimer's disease (AD) classification using brain MRI scans. The novel method achieves high accuracy, offering a promising tool for early AD detection and diagnosis.

Keywords:
ANNAlzheimer’s diseasePCASWLDAclassificationfeature extractionfeature selectionhealthcaremedical imagingneuroimaging

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Alzheimer's disease (AD) diagnosis relies on accurate and efficient methods.
  • Machine learning shows promise for AD detection but requires high accuracy and universality.
  • Current methods may lack the speed and broad applicability needed for clinical settings.

Purpose of the Study:

  • To develop and evaluate a hybrid machine learning methodology for classifying Alzheimer's disease.
  • To enhance the accuracy, reduce processing time, and improve the universality of AD classification using brain MRI.
  • To validate the proposed hybrid approach on diverse, publicly available datasets.

Main Methods:

  • A hybrid approach combining averaging filter preprocessing, principal component analysis (PCA) with stepwise linear discriminant analysis (SWLDA) for feature selection, and an artificial neural network (ANN) for classification.
  • SWLDA utilized forward and backward recursion for optimal feature subset identification.
  • The methodology was applied to brain MRI image datasets with a 10-fold cross-validation strategy.

Main Results:

  • The proposed hybrid method achieved high weighted average recognition rates of 99.35% and 96.66% across datasets.
  • The approach demonstrated superior performance compared to existing state-of-the-art systems.
  • The combination of PCA, SWLDA, and ANN proved effective in differentiating AD classes.

Conclusions:

  • The developed hybrid machine learning methodology offers a highly accurate and efficient solution for Alzheimer's disease classification.
  • This approach shows significant potential for clinical application in AD diagnosis.
  • The study highlights the effectiveness of combining advanced feature selection and classification techniques for neuroimaging analysis.