Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer's disease, Parkinson's …
Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an
important role in understanding brain functionalities and its disorders during the last couple …
important role in understanding brain functionalities and its disorders during the last couple …
Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
Braingnn: Interpretable brain graph neural network for fmri analysis
Understanding which brain regions are related to a specific neurological disorder or
cognitive stimuli has been an important area of neuroimaging research. We propose …
cognitive stimuli has been an important area of neuroimaging research. We propose …
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future …
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …
A survey on deep learning for neuroimaging-based brain disorder analysis
Deep learning has recently been used for the analysis of neuroimages, such as structural
magnetic resonance imaging (MRI), functional MRI, and positron emission tomography …
magnetic resonance imaging (MRI), functional MRI, and positron emission tomography …
Deep learning for brain disorder diagnosis based on fMRI images
In modern neuroscience and clinical study, neuroscientists and clinicians often use non-
invasive imaging techniques to validate theories and computational models, observe brain …
invasive imaging techniques to validate theories and computational models, observe brain …
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 …
[HTML][HTML] Going deep into schizophrenia with artificial intelligence
Despite years of research, the mechanisms governing the onset, relapse, symptomatology,
and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to …
and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to …
Major depressive disorder classification based on different convolutional neural network models: deep learning approach
The human brain is characterized by complex structural, functional connections that
integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation …
integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation …
Detecting neurodegenerative disease from MRI: a brief review on a deep learning perspective
Rapid development of high speed computing devices and infrastructure along with improved
understanding of deep machine learning techniques during the last decade have opened up …
understanding of deep machine learning techniques during the last decade have opened up …