Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …
performance. Currently, deep learning architectures are showing better performance …
Automatic detection of Alzheimer's disease using deep learning models and neuro-imaging: current trends and future perspectives
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 …
Automatic detection of Alzheimer's disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers
Predicting Alzheimer's disease (AD) progression is crucial for improving the management of
this chronic disease. Usually, data from AD patients are multimodal and time series in …
this chronic disease. Usually, data from AD patients are multimodal and time series in …
FDN-ADNet: Fuzzy LS-TWSVM based deep learning network for prognosis of the Alzheimer's disease using the sagittal plane of MRI scans
Alzheimer's disease (AD) is the most pervasive form of dementia, resulting in severe
psychosocial effects such as affecting personality, reasoning, emotions, and memory …
psychosocial effects such as affecting personality, reasoning, emotions, and memory …
An efficient detection and classification of acute leukemia using transfer learning and orthogonal softmax layer-based model
For the early diagnosis of hematological disorders like blood cancer, microscopic analysis of
blood cells is very important. Traditional deep CNNs lead to overfitting when it receives …
blood cells is very important. Traditional deep CNNs lead to overfitting when it receives …
[Retracted] An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network
Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading
to a steady deterioration in cognitive ability. Deep learning models have shown outstanding …
to a steady deterioration in cognitive ability. Deep learning models have shown outstanding …
Multimodal neuroimaging based Alzheimer's disease diagnosis using evolutionary RVFL classifier
Alzheimer's disease (AD) is one of the most known causes of dementia which can be
characterized by continuous deterioration in the cognitive skills of elderly people. It is a non …
characterized by continuous deterioration in the cognitive skills of elderly people. It is a non …
A systematic literature review on the significance of deep learning and machine learning in predicting Alzheimer's disease
Background Alzheimer's disease (AD) is the most prevalent cause of dementia,
characterized by a steady decline in mental, behavioral, and social abilities and impairs a …
characterized by a steady decline in mental, behavioral, and social abilities and impairs a …
Deep-learning-based diagnosis and prognosis of Alzheimer's disease: A comprehensive review
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most
common cause of Dementia. Neuroimaging analyses, such as T1 weighted magnetic …
common cause of Dementia. Neuroimaging analyses, such as T1 weighted magnetic …
Deep Q network–driven task offloading for efficient multimedia data analysis in edge computing–assisted IoV
With the prosperity of Industry 4.0, numerous emerging industries continue to gain popularity
and their market scales are expanding ceaselessly. The Internet of Vehicles (IoV), one of the …
and their market scales are expanding ceaselessly. The Internet of Vehicles (IoV), one of the …