[HTML][HTML] A comprehensive review on ensemble deep learning: Opportunities and challenges
A Mohammed, R Kora - Journal of King Saud University-Computer and …, 2023 - Elsevier
In machine learning, two approaches outperform traditional algorithms: ensemble learning
and deep learning. The former refers to methods that integrate multiple base models in the …
and deep learning. The former refers to methods that integrate multiple base models in the …
Machine learning techniques for the diagnosis of Alzheimer's disease: A review
Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the
elderly population. Efficient automated techniques are needed for early diagnosis of …
elderly population. Efficient automated techniques are needed for early diagnosis of …
Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided
diagnosis of neurodegenerative disorders, eg, Alzheimer's disease (AD), due to its …
diagnosis of neurodegenerative disorders, eg, Alzheimer's disease (AD), due to its …
Early detection of Alzheimer's disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning
Early detection is critical for effective management of Alzheimer's disease (AD) and
screening for mild cognitive impairment (MCI) is common practice. Among several deep …
screening for mild cognitive impairment (MCI) is common practice. Among several deep …
A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages
Neuroimaging has made it possible to measure pathological brain changes associated with
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …
attention in recent years. Using a variety of neuroimaging modalities such as structural …
Multi-modality cascaded convolutional neural networks for Alzheimer's disease diagnosis
M Liu, D Cheng, K Wang, Y Wang… - Neuroinformatics, 2018 - Springer
Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient
care and development of future treatment. Structural and functional neuroimages, such as …
care and development of future treatment. Structural and functional neuroimages, such as …
A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification
J Zhang, B Zheng, A Gao, X Feng, D Liang… - Magnetic Resonance …, 2021 - Elsevier
Purpose Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative
disease. In recent years, machine learning methods have been widely used on analysis of …
disease. In recent years, machine learning methods have been widely used on analysis of …
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 …
MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer's disease: a survey
N Yamanakkanavar, JY Choi, B Lee - Sensors, 2020 - mdpi.com
Many neurological diseases and delineating pathological regions have been analyzed, and
the anatomical structure of the brain researched with the aid of magnetic resonance imaging …
the anatomical structure of the brain researched with the aid of magnetic resonance imaging …