Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia
Alzheimer's disease (AD) is one of the most common form of dementia which mostly affects
elderly people. AD identification in early stages is a difficult task in medical practice and …
elderly people. AD identification in early stages is a difficult task in medical practice and …
Machine learning and deep learning approaches for brain disease diagnosis: principles and recent advances
Brain is the controlling center of our body. With the advent of time, newer and newer brain
diseases are being discovered. Thus, because of the variability of brain diseases, existing …
diseases are being discovered. Thus, because of the variability of brain diseases, existing …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
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 …
Diagnostic of autism spectrum disorder based on structural brain MRI images using, grid search optimization, and convolutional neural networks
In this study, an automatic autism diagnostic model based on sMRI is proposed. This
proposed model consists of two basic stages. The first stage is the preprocessing stage …
proposed model consists of two basic stages. The first stage is the preprocessing stage …
Deep learning-based diagnosis of Alzheimer's disease
Alzheimer's disease (AD), the most familiar type of dementia, is a severe concern in modern
healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth …
healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth …
Uncertainty-guided voxel-level supervised contrastive learning for semi-supervised medical image segmentation
Semi-supervised learning reduces overfitting and facilitates medical image segmentation by
regularizing the learning of limited well-annotated data with the knowledge provided by a …
regularizing the learning of limited well-annotated data with the knowledge provided by a …
Diagnosis of Alzheimer's disease via an attention-based multi-scale convolutional neural network
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. Accurate
diagnosis of mild cognitive impairment (MCI) in the prodromal stage of AD can delay onset …
diagnosis of mild cognitive impairment (MCI) in the prodromal stage of AD can delay onset …
Multi-modal data Alzheimer's disease detection based on 3D convolution
Z Kong, M Zhang, W Zhu, Y Yi, T Wang… - … Signal Processing and …, 2022 - Elsevier
Multi-modal medical imaging information has been widely used in computer-assisted
investigations and diagnoses. A typical example is that the combination of information from …
investigations and diagnoses. A typical example is that the combination of information from …
Impact of eeg parameters detecting dementia diseases: A systematic review
LM Sánchez-Reyes, J Rodríguez-Reséndiz… - IEEE …, 2021 - ieeexplore.ieee.org
Dementia diseases are increasing rapidly, according to the World Health Organization
(WHO), becoming an alarming problem for the health sector. The electroencephalogram …
(WHO), becoming an alarming problem for the health sector. The electroencephalogram …