[HTML][HTML] Application of deep learning for prediction of alzheimer's disease in PET/MR imaging
Y Zhao, Q Guo, Y Zhang, J Zheng, Y Yang, X Du… - Bioengineering, 2023 - mdpi.com
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of
people worldwide. Positron emission tomography/magnetic resonance (PET/MR) imaging is …
people worldwide. Positron emission tomography/magnetic resonance (PET/MR) imaging is …
A review of artificial intelligence methods for Alzheimer's disease diagnosis: Insights from neuroimaging to sensor data analysis
Alzheimer's disease is the most common cause of dementia, gradually impairing memory,
intellectual, learning, and organizational capacities. An individual's capacity to perform …
intellectual, learning, and organizational capacities. An individual's capacity to perform …
An end-to-end multimodal 3D CNN framework with multi-level features for the prediction of mild cognitive impairment
Y Zhang, X He, Y Liu, CZL Ong, Y Liu… - Knowledge-Based Systems, 2023 - Elsevier
In recent years, deep learning methods based on brain image have been used for the
diagnosis of cognitive impairment-related disorders. With the development of neuroimaging …
diagnosis of cognitive impairment-related disorders. With the development of neuroimaging …
A hybrid multimodal machine learning model for Detecting Alzheimer's disease
J Sheng, Q Zhang, Q Zhang, L Wang, Z Yang… - Computers in Biology …, 2024 - Elsevier
Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has
limitations. Multimodal fusion of complementary biomarkers may improve diagnostic …
limitations. Multimodal fusion of complementary biomarkers may improve diagnostic …
A Dimension Centric Proximate Attention Network and Swin Transformer for Age-Based Classification of Mild Cognitive Impairment From Brain MRI
T Illakiya, R Karthik - IEEE Access, 2023 - ieeexplore.ieee.org
The early identification and treatment of Mild Cognitive Impairment (MCI) play a crucial role
in managing the risk of Alzheimer's disease (AD). However, current methods for categorizing …
in managing the risk of Alzheimer's disease (AD). However, current methods for categorizing …
HOPE: Hybrid-Granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment
Mild cognitive impairment (MCI) is often at high risk of progression to Alzheimer's disease
(AD). Existing works to identify the progressive MCI (pMCI) typically require MCI subtype …
(AD). Existing works to identify the progressive MCI (pMCI) typically require MCI subtype …
[HTML][HTML] Bio-inspired Deep Learning-Personalized Ensemble Alzheimer's Diagnosis Model for Mental Well-being
Most classification models for Alzheimer's Diagnosis (AD) do not have specific strategies for
individual input samples, leading to the problem of easily overlooking personalized …
individual input samples, leading to the problem of easily overlooking personalized …
A 119.64 GOPs/W FPGA-Based ResNet50 Mixed-Precision Accelerator Using the Dynamic DSP Packing
This brief presents a precision-sensitivity-aware quantization (PSAQ) mixed precision (MP)
compression scheme designed for both weights and activations. The PSAQ MP method …
compression scheme designed for both weights and activations. The PSAQ MP method …
[PDF][PDF] SLAS Technology
Most classification models for Alzheimer's Diagnosis (AD) do not have specific strategies for
individual input samples, leading to the problem of easily overlooking personalized …
individual input samples, leading to the problem of easily overlooking personalized …