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Related Concept Videos

Alzheimer's Disease: Overview01:26

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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.
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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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InGSA: integrating generalized self-attention in CNN for Alzheimer's disease classification.

Faisal Binzagr1, Anas W Abulfaraj2

  • 1Department of Computer Science, King Abdulaziz University, Rabigh, Saudi Arabia.

Frontiers in Artificial Intelligence
|March 27, 2025
PubMed
Summary

This study introduces InGSA, a novel deep learning model for early Alzheimer's disease diagnosis from MRI scans. It improves accuracy by enhancing image contrast and using a generalized self-attention mechanism to identify disease patterns effectively.

Keywords:
Alzheimer's disease classificationCNNgeneralized self-attentiontransfer learningtransformer

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Alzheimer's disease (AD) is a progressive neurodegenerative disorder impacting cognitive functions.
  • Early diagnosis of AD is crucial for symptom management, yet traditional methods face limitations.
  • Existing machine learning and convolutional neural network (CNN) approaches struggle with AD identification from MRI scans due to complex feature extraction and specificity issues.

Purpose of the Study:

  • To develop an advanced deep learning framework for accurate multiclass Alzheimer's disease classification using MRI data.
  • To overcome the limitations of traditional feature extraction methods in AD diagnosis.
  • To introduce a novel contrast enhancement technique and a CNN-transformer model for improved AD detection.

Main Methods:

  • Implementation of a haze-reduced local-global (HRLG) contrast enhancement approach for MRI scans.
  • Development of a global CNN-transformer model named InGSA, based on the pre-trained InceptionV3 architecture.
  • Integration of a generalized self-attention (GSA) block within the InGSA model to capture spatial and channel-wise feature interactions and suppress noise.

Main Results:

  • The proposed InGSA model demonstrated superior performance in multiclass AD classification on two benchmark datasets.
  • The GSA module effectively captured intricate details of AD-related information while mitigating noise.
  • Evaluation using various pre-trained networks confirmed the effectiveness of the GSA mechanism.

Conclusions:

  • The InGSA framework, incorporating HRLG contrast enhancement and a GSA module, offers a significant advancement in Alzheimer's disease diagnosis from MRI.
  • This deep learning approach overcomes the challenges associated with traditional methods, providing higher specificity and efficiency.
  • The findings suggest a promising direction for developing more accurate and reliable tools for early AD detection.