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Alzheimer's disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions
Alzheimer's Disease (AD) is the most prevalent and rapidly progressing neurodegenerative
disorder among the elderly and is a leading cause of dementia. AD results in significant …
disorder among the elderly and is a leading cause of dementia. AD results in significant …
[HTML][HTML] A deep learning based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer Disease using MRI scans
Alzheimer's disease (AD) is one of the most prevalent types of dementia, which primarily
affects people over age 60. In clinical practice, it is a challenging task to identify AD in its …
affects people over age 60. In clinical practice, it is a challenging task to identify AD in its …
Going beyond established model systems of Alzheimer's disease: companion animals provide novel insights into the neurobiology of aging
Alzheimer's disease (AD) is characterized by brain plaques, tangles, and cognitive
impairment. AD is one of the most common age-related dementias in humans. Progress in …
impairment. AD is one of the most common age-related dementias in humans. Progress in …
Efficient diagnosis of autism spectrum disorder using optimized machine learning models based on structural MRI
Autism spectrum disorder (ASD) affects approximately 1.4% of the population and imposes
significant social and economic burdens. Because its etiology is unknown, effective …
significant social and economic burdens. Because its etiology is unknown, effective …
[HTML][HTML] Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA
The examination of Alzheimer's disease (AD) using adaptive machine learning algorithms
has unveiled promising findings. However, achieving substantial credibility in medical …
has unveiled promising findings. However, achieving substantial credibility in medical …
Development of a robust parallel and multi-composite machine learning model for improved diagnosis of Alzheimer's disease: correlation with dementia-associated …
Introduction Machine learning (ML) algorithms and statistical modeling offer a potential
solution to offset the challenge of diagnosing early Alzheimer's disease (AD) by leveraging …
solution to offset the challenge of diagnosing early Alzheimer's disease (AD) by leveraging …
Alzheimer Disease Progression Forecasting: Empowering Models Through hybrid of CNN and LSTM with PSO Op-Timization
A common neurodegenerative disease, Alzheimer Disease (AD) affects society. Early
intervention and personalized care require accurate condition prediction. A hybrid model …
intervention and personalized care require accurate condition prediction. A hybrid model …
[HTML][HTML] Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis
Background To diagnose Alzheimer disease (AD), individuals are classified according to the
severity of their cognitive impairment. There are currently no specific causes or conditions for …
severity of their cognitive impairment. There are currently no specific causes or conditions for …
Multimodal fusion diagnosis of the Alzheimer's disease via lightweight CNN-LSTM model using magnetic resonance imaging (MRI)
EU Haq, Q Yong, Z Yuan, X Huarong… - … Signal Processing and …, 2025 - Elsevier
Alzheimer's disease is categorized as a primary neurodegenerative ailment that mostly
affects individuals in the elderly age and those reaching later stages of life. The recognition …
affects individuals in the elderly age and those reaching later stages of life. The recognition …
Exploring Integration of Multimodal Deep Learning Approaches for Enhanced Alzheimer's Disease Diagnosis: A Review of Recent Literature
Abstract Alzheimer's disease (AD), is the most common form of dementia that affects the
nervous system. In the past few years, non-invasive early AD diagnosis has become more …
nervous system. In the past few years, non-invasive early AD diagnosis has become more …