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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...

Alzheimer's Disease: Treatment

Alzheimer's Disease: Treatment

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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Updated: Jun 23, 2025

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

Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer's Disease Detection.

Najmul Hassan¹, Abu Saleh Musa Miah¹, Jungpil Shin¹

¹School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan.

Journal of Imaging

|June 26, 2024

Summary

A new multi-stage deep neural network accurately detects Alzheimer's Disease (AD). This advanced system shows high accuracy, offering a significant improvement for early AD detection and analysis in medical imaging.

Keywords:

Alzheimer’s disease CNN Random Forest machine learning residual network

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Neurology
Medical Imaging
Artificial Intelligence

Background:

Alzheimer's Disease (AD) is a leading cause of dementia globally, affecting over 50% of elderly Japanese.
Current AD detection methods struggle with the complexity of deep learning models.
There is a critical need for automated and accurate AD detection systems.

Purpose of the Study:

To introduce a novel multi-stage deep neural network for enhanced Alzheimer's Disease detection.
To address the limitations of existing hierarchical convolutional neural networks (CNNs) in AD analysis.
To improve the accuracy and efficiency of automated AD detection systems.

Main Methods:

A five-stage deep neural network architecture utilizing residual functions for feature enhancement.
Integration of a deep learning-based feature selection module with batch normalization, dropout, and fully connected layers to prevent overfitting.
Classification using machine learning algorithms: Support Vector Machines (SVM), Random Forest (RF), and SoftMax.

Main Results:

The proposed model achieved high accuracy rates: 99.47% on ADNI1, 99.10% on MIRAID, and 99.70% on OASIS Kaggle datasets.
The system demonstrated superior performance compared to existing methods in binary classification tasks.
The multi-stage architecture effectively enhanced feature extraction and model depth.

Conclusions:

The novel multi-stage deep neural network offers a significant advancement in Alzheimer's Disease analysis.
The model's high accuracy suggests its potential for reliable automated AD detection.
This approach paves the way for more effective early diagnosis and management of Alzheimer's Disease.