[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 …
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 …
Going beyond established model systems of Alzheimer's disease: companion animals provide novel insights into the neurobiology of aging
AA de Sousa, BA Rigby Dames, EC Graff… - Communications …, 2023 - nature.com
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 …
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 …
Alzheimer Disease Progression Forecasting: Empowering Models Through hybrid of CNN and LSTM with PSO Op-Timization
P Deshpande, R Dhabliya, D Khubalkar… - … on Emerging Smart …, 2024 - ieeexplore.ieee.org
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 …
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 …
A Comparative Analysis of Machine Learning Algorithms for the Early Prediction of Cardiovascular Disease
IU Haq, AH Rather, G Kaur - 2023 2nd International Conference …, 2023 - ieeexplore.ieee.org
Early diagnosis of cardiovascular disease signs and continued medical therapy can lower
death rates and the number of afflicted people. As there are more occurrences of …
death rates and the number of afflicted people. As there are more occurrences of …
Alzheimer's disease detection from magnetic resonance imaging: a deep learning perspective
K Armonaite, M La Ventura… - Exploration of …, 2023 - explorationpub.com
Aim: Up to date many successful attempts to identify various types of lesions with machine
learning (ML) were made, however, the recognition of Alzheimer's disease (AD) from brain …
learning (ML) were made, however, the recognition of Alzheimer's disease (AD) from brain …
Diagnosis of Alzheimer's Disease Using Boosting Classification Algorithms
Alzheimer's Disease (AD) is a progressive degenerative disorder of the brain that impacts
memory, cognition, and, ultimately, the ability to carry out daily activities. There is presently …
memory, cognition, and, ultimately, the ability to carry out daily activities. There is presently …
Comparison of the Performance of CNN Transfer Learning in the Classification of Alzheimer's Disease
Alzheimer's disease is a progressive neurodegenerative condition followed by a psychiatric,
cognitive, and structural decline, accounting for 60%–80% of all dementia cases. The …
cognitive, and structural decline, accounting for 60%–80% of all dementia cases. The …