Multimodal machine learning in precision health: A sco** review
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …
sector including utilization for clinical decision-support. Its use has historically been focused …
Deep learning for Alzheimer's disease diagnosis: A survey
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 …
Analysis of features of Alzheimer's disease: Detection of early stage from functional brain changes in magnetic resonance images using a finetuned ResNet18 network
One of the first signs of Alzheimer's disease (AD) is mild cognitive impairment (MCI), in
which there are small variants of brain changes among the intermediate stages. Although …
which there are small variants of brain changes among the intermediate stages. Although …
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 …
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 …
Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson's disease
Background and objectives Parkinson's Disease (PD) is a devastating chronic neurological
condition. Machine learning (ML) techniques have been used in the early prediction of PD …
condition. Machine learning (ML) techniques have been used in the early prediction of PD …
[PDF][PDF] RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data …
SH Wang, Q Zhou, M Yang, YD Zhang - Frontiers in Aging …, 2021 - frontiersin.org
Aim: Alzheimer's disease is a neurodegenerative disease that causes 60–70% of all cases
of dementia. This study is to provide a novel method that can identify AD more accurately …
of dementia. This study is to provide a novel method that can identify AD more accurately …
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 …
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 …
Artificial intelligence-enabled digital transformation in elderly healthcare field: sco** review
As the ageing population grows continuously, traditional healthcare providers are
experiencing difficulty in kee** up with changing and unpredictable demands as well as …
experiencing difficulty in kee** up with changing and unpredictable demands as well as …