[HTML][HTML] Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review
Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation
and cognitive function impairment in elderly people. The irreversible and devastating …
and cognitive function impairment in elderly people. The irreversible and devastating …
Automatic detection of Alzheimer's disease using deep learning models and neuro-imaging: current trends and future perspectives
T Illakiya, R Karthik - Neuroinformatics, 2023 - Springer
Deep learning algorithms have a huge influence on tackling research issues in the field of
medical image processing. It acts as a vital aid for the radiologists in producing accurate …
medical image processing. It acts as a vital aid for the radiologists in producing accurate …
A new deep belief network-based multi-task learning for diagnosis of Alzheimer's disease
Accurate classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI),
especially distinguishing the progressive MCI (pMCI) from stable MCI (sMCI), will be helpful …
especially distinguishing the progressive MCI (pMCI) from stable MCI (sMCI), will be helpful …
Transfer learning using freeze features for Alzheimer neurological disorder detection using ADNI dataset
S Naz, A Ashraf, A Zaib - Multimedia Systems, 2022 - Springer
Abstract Machine learning and deep learning play a crucial role in identification of various
diseases like neurological, skin, eyes, blood and cancers. The deep learning algorithms can …
diseases like neurological, skin, eyes, blood and cancers. The deep learning algorithms can …
[HTML][HTML] Applications of artificial intelligence to aid early detection of dementia: a scoping review on current capabilities and future directions
Abstract Background & Objective With populations aging, the number of people with
dementia worldwide is expected to triple to 152 million by 2050. Seventy percent of cases …
dementia worldwide is expected to triple to 152 million by 2050. Seventy percent of cases …
Electrical activity and synchronization of memristor synapse-coupled HR network based on energy method
Y Lu, H Li, C Li - Neurocomputing, 2023 - Elsevier
Electrical synapses and external stimuli can affect the exchange and propagation of field
energy between neurons, and thus induce a variety of dynamics and electricity activities of …
energy between neurons, and thus induce a variety of dynamics and electricity activities of …
A single model deep learning approach for Alzheimer's disease diagnosis
Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild
cognitive impairment (MCI) is essential for the delayed disease progression and the …
cognitive impairment (MCI) is essential for the delayed disease progression and the …
A comprehensive survey on the detection, classification, and challenges of neurological disorders
Simple Summary This study represents a resourceful review article that can deliver
resources on neurological diseases and their implemented classification algorithms to …
resources on neurological diseases and their implemented classification algorithms to …
Transfer learning approaches for neuroimaging analysis: a scoping review
Deep learning algorithms have been moderately successful in diagnoses of diseases by
analyzing medical images especially through neuroimaging that is rich in annotated data …
analyzing medical images especially through neuroimaging that is rich in annotated data …
Early-stage Alzheimer's disease categorization using PET neuroimaging modality and convolutional neural networks in the 2D and 3D domains
Alzheimer's Disease (AD) is a health apprehension of significant proportions that is
negatively impacting the ageing population globally. It is characterized by neuronal loss and …
negatively impacting the ageing population globally. It is characterized by neuronal loss and …