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Alzheimer's Disease: Overview

Alzheimer's Disease: Overview

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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Updated: Feb 17, 2026

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

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

Published on: December 15, 2023

Explainable bidirectional encoder representations from image transformers for Alzheimer's disease prediction.

Sheikh Muhammad Saqib¹, Mona A Alkhattabi², Muhammad Amir Khan³

¹Department of Computing and Information Technology, Gomal University, Dera Ismail Khan, Pakistan.

|February 16, 2026

Summary

This study introduces an AI framework using Bidirectional-Encoder representations from Image Transformers (BEiT) for accurate Alzheimer's disease (AD) classification from MRI scans. The model achieved high accuracy, aiding early diagnosis and intervention strategies.

Keywords:

Alzheimer's disease (AD)artificial intelligence (AI)bidirectional-encoder explainable AI (XAI)local interpretable model-agnostic explanations (LIME)

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Last Updated: Feb 17, 2026

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

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

Published on: December 15, 2023

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

Area of Science:

Artificial Intelligence in Medicine
Neuroimaging Analysis
Machine Learning for Diagnostics

Background:

Alzheimer's disease (AD) causes progressive neurological decline, impacting cognition, behavior, and quality of life for patients and caregivers.
Early and precise diagnosis of AD is crucial for implementing effective intervention strategies.
Artificial intelligence (AI) shows significant promise in medical imaging for AD detection and classification.

Purpose of the Study:

To develop and evaluate an explainable transformer-based AI framework for automated AD stage classification.
To leverage Bidirectional-Encoder representations from Image Transformers (BEiT) for analyzing magnetic resonance imaging (MRI) brain scans.
To enhance the precision of AD diagnosis through advanced machine learning techniques.

Main Methods:

Utilized a dataset of 8511 MRI brain images across three diagnostic groups: mild, moderate, and no impairment.
Employed BEiT as a feature extractor within the proposed AI framework.
Addressed class imbalance using a Wasserstein generative adversarial network with gradient penalty for synthetic MRI image generation and data augmentation.

Main Results:

Achieved outstanding classification accuracy of 96%.
Reported high F1-scores: 0.94 (mild AD), 1.00 (moderate AD), and 0.95 (no AD).
Demonstrated strong performance with a mean absolute error of 0.0727, Cohen's kappa of 0.9451, Matthews correlation coefficient of 0.9455, and Hamming loss of 0.0365.

Conclusions:

The developed explainable transformer-based framework demonstrates high efficacy in classifying AD stages from MRI scans.
The AI model's performance indicates its potential as a valuable tool for early and accurate Alzheimer's disease diagnosis.
The study highlights the significant role of advanced AI techniques, like BEiT, in neuroimaging for neurodegenerative disease assessment.