Review on alzheimer disease detection methods: Automatic pipelines and machine learning techniques
Alzheimer's Disease (AD) is becoming increasingly prevalent across the globe, and various
diagnostic and detection methods have been developed in recent years. Several techniques …
diagnostic and detection methods have been developed in recent years. Several techniques …
A systematic literature review on multimodal machine learning: Applications, challenges, gaps and future directions
Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …
heterogeneous data from multiple modalities and machine learning (ML) are combined to …
A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease
Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and
progression detection have been intensively studied. Nevertheless, research studies often …
progression detection have been intensively studied. Nevertheless, research studies often …
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 …
Ensemble transfer learning-based multimodal sentiment analysis using weighted convolutional neural networks
Huge amounts of multimodal content and comments in a mixture form of text, image, and
emoji are continuously shared by users on various social networks. Most of the comments of …
emoji are continuously shared by users on various social networks. Most of the comments of …
Prediction of Alzheimer's progression based on multimodal deep-learning-based fusion and visual explainability of time-series data
Alzheimer's disease (AD) is a neurological illness that causes cognitive impairment and has
no known treatment. The premise for delivering timely therapy is the early diagnosis of AD …
no known treatment. The premise for delivering timely therapy is the early diagnosis of AD …
Two-stage deep learning model for Alzheimer's disease detection and prediction of the mild cognitive impairment time
Alzheimer's disease (AD) is an irreversible neurodegenerative disease characterized by
thinking, behavioral and memory impairments. Early prediction of conversion from mild …
thinking, behavioral and memory impairments. Early prediction of conversion from mild …
Robust hybrid deep learning models for Alzheimer's progression detection
The prevalence of Alzheimer's disease (AD) in the growing elderly population makes
accurately predicting AD progression crucial. Due to AD's complex etiology and …
accurately predicting AD progression crucial. Due to AD's complex etiology and …
Multimodal attention-based deep learning for Alzheimer's disease diagnosis
Objective Alzheimer's disease (AD) is the most common neurodegenerative disorder with
one of the most complex pathogeneses, making effective and clinically actionable decision …
one of the most complex pathogeneses, making effective and clinically actionable decision …
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 …