[HTML][HTML] Alzheimer's disease detection from structural MRI using conditional deep triplet network
Alzheimer's disease (AD) as an advanced brain disorder may cause damage to the memory
and tissue loss in the brain. Since AD is a mostly costly disease, various deep learning …
and tissue loss in the brain. Since AD is a mostly costly disease, various deep learning …
Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review
Objective Alzheimer disease (AD) is the most common cause of dementia, a syndrome
characterized by cognitive impairment severe enough to interfere with activities of daily life …
characterized by cognitive impairment severe enough to interfere with activities of daily life …
Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network
Abstract (Aim) COVID-19 is an infectious disease spreading to the world this year. In this
study, we plan to develop an artificial intelligence based tool to diagnose on chest CT …
study, we plan to develop an artificial intelligence based tool to diagnose on chest CT …
COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis
Aim: COVID-19 is a disease caused by a new strain of coronavirus. Up to 18th October
2020, worldwide there have been 39.6 million confirmed cases resulting in more than 1.1 …
2020, worldwide there have been 39.6 million confirmed cases resulting in more than 1.1 …
AlzheimerNet: An effective deep learning based proposition for alzheimer's disease stages classification from functional brain changes in magnetic resonance images
Alzheimer's disease is largely the underlying cause of dementia due to its progressive
neurodegenerative nature among the elderly. The disease can be divided into five stages …
neurodegenerative nature among the elderly. The disease can be divided into five stages …
A deep Siamese convolution neural network for multi-class classification of Alzheimer disease
Alzheimer's disease (AD) may cause damage to the memory cells permanently, which
results in the form of dementia. The diagnosis of Alzheimer's disease at an early stage is a …
results in the form of dementia. The diagnosis of Alzheimer's disease at an early stage is a …
An MRI scans-based Alzheimer's disease detection via convolutional neural network and transfer learning
KT Chui, BB Gupta, W Alhalabi, FS Alzahrani - Diagnostics, 2022 - mdpi.com
Alzheimer's disease (AD) is the most common type (> 60%) of dementia and can wreak
havoc on the psychological and physiological development of sufferers and their carers, as …
havoc on the psychological and physiological development of sufferers and their carers, as …
Interpretable visual transmission lines inspections using pseudo-prototypical part network
To guarantee the reliability of the electric energy supply, it is necessary that the transmission
lines are operating without interruptions. To improve the identification of faults in the …
lines are operating without interruptions. To improve the identification of faults in the …
[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 …
[Retracted] PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID‐19 with Multiple‐Way Data Augmentation
Aim. COVID‐19 has caused large death tolls all over the world. Accurate diagnosis is of
significant importance for early treatment. Methods. In this study, we proposed a novel …
significant importance for early treatment. Methods. In this study, we proposed a novel …