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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

Alzheimer's Disease: Treatment

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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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Dementia01:30

Dementia

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Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....
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Related Experiment Video

Updated: Jul 26, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

Alzheimer's disease diagnosis and classification using deep learning techniques.

Waleed Al Shehri1

  • 1Department of Computer Science, College of Computer in Al-Lith, Umm Al-Qura University, Makkah, Saudi Arabia.

Peerj. Computer Science
|June 22, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for Alzheimer's disease diagnosis. DenseNet-169 achieved higher accuracy in classifying dementia stages, offering a potential solution for early detection.

Keywords:
Alzheimer’s diseaseCNNDementiaDenseNet169MRIResNet50

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Alzheimer's disease is a progressive neurodegenerative disorder impacting memory, primarily in individuals over 65.
  • Accurate and early diagnosis is crucial but challenging due to manual methods being time-consuming and error-prone.
  • Existing diagnostic techniques require enhancement for improved accuracy in early detection.

Purpose of the Study:

  • To develop and evaluate a deep learning model for the accurate diagnosis and classification of Alzheimer's disease stages.
  • To compare the performance of DenseNet-169 and ResNet-50 Convolutional Neural Network (CNN) architectures for this task.
  • To provide a solution for real-time analysis and classification of Alzheimer's disease.

Main Methods:

  • Utilized deep learning, specifically DenseNet-169 and ResNet-50 CNN architectures.
  • Trained and tested the models on datasets for Alzheimer's disease classification.
  • Classified patients into four categories: Non-Dementia, Very Mild Dementia, Mild Dementia, and Moderate Dementia.

Main Results:

  • The DenseNet-169 model demonstrated superior performance, achieving higher accuracy in both training (0.977) and testing (0.8382) phases.
  • The ResNet-50 model achieved training accuracy of 0.8870 and testing accuracy of 0.8192.
  • DenseNet-169 showed better efficacy in distinguishing between different stages of dementia.

Conclusions:

  • Deep learning models, particularly DenseNet-169, show significant promise for accurate and efficient Alzheimer's disease diagnosis.
  • The proposed model can aid in early detection and real-time classification, potentially improving patient management.
  • Further research can explore integrating these models into clinical workflows for broader application.