Information fusion-based Bayesian optimized heterogeneous deep ensemble model based on longitudinal neuroimaging data

N Rahim, S El-Sappagh, H Rizk, OA El-serafy… - Applied Soft …, 2024 - Elsevier
The fusion of multimodal longitudinal data is difficult but crucial for enhancing the accuracy
of deep learning models for disease identification and helps provide tailored and patient …

An explainable convolutional neural network for the early diagnosis of Alzheimer's disease from 18F-FDG PET

LA De Santi, E Pasini, MF Santarelli, D Genovesi… - Journal of Digital …, 2023 - Springer
Abstract Convolutional Neural Networks (CNN) which support the diagnosis of Alzheimer's
Disease using 18F-FDG PET images are obtaining promising results; however, one of the …

A Review of Datasets, Optimization Strategies, and Learning Algorithms for Analyzing Alzheimer's Dementia Detection

V Thulasimani, K Shanmugavadivel, J Cho… - Neuropsychiatric …, 2024 - Taylor & Francis
Alzheimer's Dementia (AD) is a progressive neurological disorder that affects memory and
cognitive function, necessitating early detection for its effective management. This poses a …

Deep Learning in Early Alzheimers diseases Detection: A Comprehensive Survey of Classification, Segmentation, and Feature Extraction Methods

R Hafeez, S Waheed, SA Naqvi, F Maqbool… - arxiv preprint arxiv …, 2025 - arxiv.org
Alzheimers disease is a deadly neurological condition, impairing important memory and
brain functions. Alzheimers disease promotes brain shrinkage, ultimately leading to …

Multi-View Separable Residual convolution neural Network for detecting Alzheimer's disease progression

MA Zayene, H Basly, FE Sayadi - Biomedical Signal Processing and …, 2024 - Elsevier
Alzheimer's Disease (AD) is a neurodegenerative disorder, the most common form of
dementia, characterized by memory loss and cognitive impairments that disrupt daily life …

A multiview-slice feature fusion network for early diagnosis of Alzheimer's disease with structural MRI images

H Huang, W Pedrycz, K Hirota, F Yan - Information Fusion, 2025 - Elsevier
Alzheimer's disease (AD) is a prevalent neurodegenerative disorder with high incidence and
significant mortality among the elderly worldwide. Nevertheless, early and accurate …

Presenting a novel approach based on deep learning neural network and using brain images to diagnose Alzheimer's disease

S Zhao, M Li, Hua**, L Yu, Y Tang - Proceedings of the Indian National …, 2023 - Springer
A major symptom of Alzheimer's disease is memory impairment, which is the most prevalent
form of dementia. The risk of Alzheimer's disease is significantly increased by brain injury …

Dementia Detection with Deep Networks Using Multi-Modal Image Data

A Yiğit, Z Işık, Y Baştanlar - Diagnosis of Neurological Disorders …, 2023 - taylorfrancis.com
Neurodegenerative diseases give rise to irreversible neural damage in the brain. By the time
it is diagnosed, the disease may have progressed. Although there is no complete treatment …

Multi-view multi-input CNN-based architecture for diagnosis of Alzheimer's disease in its prodromal stages

MA Zayene, H Basly, FE Sayadi - International Journal of …, 2024 - inderscienceonline.com
Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder, the leading
cause of dementia, characterised by memory loss and cognitive decline affecting daily life …

[PDF][PDF] USING 3D-CAPSNET AND RNN FOR ALZHEIMER'S DISEASE DETECTION BASED ON 4D fMRI

ALI ISMAIL - 2023 - researchgate.net
A.IS M AIL AT IL IM UN IV ERS IT Y 2023 Page 1 USING 3D-CAPSNET AND RNN FOR
ALZHEIMER’S DISEASE DETECTION BASED ON 4D fMRI THE GRADUATE SCHOOL OF …