Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia
Alzheimer's disease (AD) is one of the most common form of dementia which mostly affects
elderly people. AD identification in early stages is a difficult task in medical practice and …
elderly people. AD identification in early stages is a difficult task in medical practice and …
Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?
S Mirkin, BC Albensi - Frontiers in Aging Neuroscience, 2023 - frontiersin.org
Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory,
thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD …
thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD …
Hybridized deep learning approach for detecting Alzheimer's disease
Alzheimer's disease (AD) is mainly a neurodegenerative sickness. The primary
characteristics are neuronal atrophy, amyloid deposition, and cognitive, behavioral, and …
characteristics are neuronal atrophy, amyloid deposition, and cognitive, behavioral, and …
Explainable artificial intelligence of multi-level stacking ensemble for detection of Alzheimer's disease based on particle swarm optimization and the sub-scores of …
Alzheimer's disease (AD) is a progressive neurological disorder characterized by memory
loss and cognitive decline, affecting millions worldwide. Early detection is crucial for effective …
loss and cognitive decline, affecting millions worldwide. Early detection is crucial for effective …
Machine Learning Model and Cuckoo Search in a modular system to identify Alzheimer's disease from MRI scan images
S Thangavel, S Selvaraj - Computer Methods in Biomechanics and …, 2023 - Taylor & Francis
Alzheimer's disease affects the majority of the elderly in today's world. It directly affects the
neurotransmitters and leads to dementia. Brain MRI images can identify Alzheimer's …
neurotransmitters and leads to dementia. Brain MRI images can identify Alzheimer's …
Dynamic prediction in clinical survival analysis using temporal convolutional networks
Accurate prediction of disease trajectories is critical for early identification and timely
treatment of patients at risk. Conventional methods in survival analysis are often constrained …
treatment of patients at risk. Conventional methods in survival analysis are often constrained …
A robust and clinically applicable deep learning model for early detection of Alzheimer's
Alzheimer's disease, often known as dementia, is a severe neurodegenerative disorder that
causes irreversible memory loss by destroying brain cells. People die because there is no …
causes irreversible memory loss by destroying brain cells. People die because there is no …
Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data
Alzheimer's disease (AD) is the most common form of dementia. Early and accurate
detection of AD is crucial to plan for disease modifying therapies that could prevent or delay …
detection of AD is crucial to plan for disease modifying therapies that could prevent or delay …
IoT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification
K Singh, J Malhotra - Journal of Ambient Intelligence and Humanized …, 2023 - Springer
Epilepsy, a fatal neurological disorder, has been emerged as a worldwide problem and is
one of the major risks to human lives. There exists an urgent need for an efficient technique …
one of the major risks to human lives. There exists an urgent need for an efficient technique …
[HTML][HTML] Impact of the learners diversity and combination method on the generation of heterogeneous classifier ensembles
Ensembles of classifiers is a proven approach in machine learning with a wide variety of
research works. The main issue in ensembles of classifiers is not only the selection of the …
research works. The main issue in ensembles of classifiers is not only the selection of the …