Deep learning for Alzheimer's disease diagnosis: A survey
M Khojaste-Sarakhsi, SS Haghighi… - Artificial intelligence in …, 2022 - Elsevier
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a
progressive decline in cognitive abilities. Since AD starts several years before the onset of …
progressive decline in cognitive abilities. Since AD starts several years before the onset of …
The use of neuroimaging techniques in the early and differential diagnosis of dementia
L Chouliaras, JT O'Brien - Molecular Psychiatry, 2023 - nature.com
Dementia is a leading cause of disability and death worldwide. At present there is no
disease modifying treatment for any of the most common types of dementia such as …
disease modifying treatment for any of the most common types of dementia such as …
A deep CNN based multi-class classification of Alzheimer's disease using MRI
In the recent years, deep learning has gained huge fame in solving problems from various
fields including medical image analysis. This work proposes a deep convolutional neural …
fields including medical image analysis. This work proposes a deep convolutional neural …
Diagnosis of Alzheimer's disease via multi-modality 3D convolutional neural network
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. In the
last decade, studies on AD diagnosis has attached great significance to artificial intelligence …
last decade, studies on AD diagnosis has attached great significance to artificial intelligence …
Automatic detection of Alzheimer's disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers
Predicting Alzheimer's disease (AD) progression is crucial for improving the management of
this chronic disease. Usually, data from AD patients are multimodal and time series in …
this chronic disease. Usually, data from AD patients are multimodal and time series in …
Effective feature learning and fusion of multimodality data using stage‐wise deep neural network for dementia diagnosis
In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic
data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive …
data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive …
A CNN based framework for classification of Alzheimer's disease
In the current decade, advances in health care are attracting widespread interest due to their
contributions to people longer surviving and fitter lives. Alzheimer's disease (AD) is the …
contributions to people longer surviving and fitter lives. Alzheimer's disease (AD) is the …
Alzheimer's disease detection through whole-brain 3D-CNN MRI
The projected burden of dementia by Alzheimer's disease (AD) represents a looming
healthcare crisis as the population of most countries grows older. Although there is currently …
healthcare crisis as the population of most countries grows older. Although there is currently …
[HTML][HTML] Structural neuroimaging as clinical predictor: A review of machine learning applications
In this paper, we provide an extensive overview of machine learning techniques applied to
structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We …
structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We …
[HTML][HTML] Advancements in computer-assisted diagnosis of Alzheimer's disease: A comprehensive survey of neuroimaging methods and AI techniques for early …
Alzheimer's Disease (AD) is a brain disorder that causes the brain to shrink and eventually
causes brain cells to die. This neurological condition progressively hampers cognitive and …
causes brain cells to die. This neurological condition progressively hampers cognitive and …