[HTML][HTML] Multimodal deep learning models for early detection of Alzheimer's disease stage
Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single
data modality to make predictions such as AD stages. The fusion of multiple data modalities�…
data modality to make predictions such as AD stages. The fusion of multiple data modalities�…
[HTML][HTML] Deep learning in Alzheimer's disease: diagnostic classification and prognostic prediction using neuroimaging data
Deep learning, a state-of-the-art machine learning approach, has shown outstanding
performance over traditional machine learning in identifying intricate structures in complex�…
performance over traditional machine learning in identifying intricate structures in complex�…
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�…
[HTML][HTML] Ensemble learning using traditional machine learning and deep neural network for diagnosis of Alzheimer's disease
In recent years, Alzheimer's disease (AD) diagnosis using neuroimaging and deep learning
has drawn great research attention. However, due to the scarcity of training neuroimaging�…
has drawn great research attention. However, due to the scarcity of training neuroimaging�…
[HTML][HTML] Predicting Alzheimer's disease progression using multi-modal deep learning approach
Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline
in cognitive functions with no validated disease modifying treatment. It is critical for timely�…
in cognitive functions with no validated disease modifying treatment. It is critical for timely�…
A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease
Some forms of mild cognitive impairment (MCI) are the clinical precursors of Alzheimer's
disease (AD), while other MCI types tend to remain stable over-time and do not progress to�…
disease (AD), while other MCI types tend to remain stable over-time and do not progress to�…
DEMNET: A deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images
S Murugan, C Venkatesan, MG Sumithra, XZ Gao…�- Ieee�…, 2021 - ieeexplore.ieee.org
Alzheimer's Disease (AD) is the most common cause of dementia globally. It steadily
worsens from mild to severe, impairing one's ability to complete any work without assistance�…
worsens from mild to severe, impairing one's ability to complete any work without assistance�…
Multimodal inductive transfer learning for detection of Alzheimer's dementia and its severity
Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising
rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost�…
rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost�…
[HTML][HTML] A deep learning MRI approach outperforms other biomarkers of prodromal Alzheimer's disease
Background The three core pathologies of Alzheimer's disease (AD) are amyloid pathology,
tau pathology, and neurodegeneration. Biomarkers exist for each. Neurodegeneration is�…
tau pathology, and neurodegeneration. Biomarkers exist for each. Neurodegeneration is�…
Early prediction of Alzheimer's disease dementia based on baseline hippocampal MRI and 1-year follow-up cognitive measures using deep recurrent neural networks
Multi-modal biological, imaging, and neuropsychological markers have demonstrated
promising performance for distinguishing Alzheimer's disease (AD) patients from cognitively�…
promising performance for distinguishing Alzheimer's disease (AD) patients from cognitively�…