Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review
Abstract Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective
rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) …
rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) …
Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review
Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and
symptoms that appear in early childhood. ASD is also associated with communication …
symptoms that appear in early childhood. ASD is also associated with communication …
MVS-GCN: A prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis
Purpose Recently, functional brain networks (FBN) have been used for the classification of
neurological disorders, such as Autism Spectrum Disorders (ASD). Neurological disorder …
neurological disorders, such as Autism Spectrum Disorders (ASD). Neurological disorder …
Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction
Purpose Recently, brain connectivity networks have been used for the classification of
neurological disorder, such as Autism Spectrum Disorders (ASD) or Alzheimer's disease …
neurological disorder, such as Autism Spectrum Disorders (ASD) or Alzheimer's disease …
Eye tracking-based diagnosis and early detection of autism spectrum disorder using machine learning and deep learning techniques
Eye tracking is a useful technique for detecting autism spectrum disorder (ASD). One of the
most important aspects of good learning is the ability to have atypical visual attention. The …
most important aspects of good learning is the ability to have atypical visual attention. The …
rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis
Abstract Autism Spectrum Disorder (ASD) diagnosis is still based on behavioral criteria
through a lengthy and time-consuming process. Much effort is being made to identify brain …
through a lengthy and time-consuming process. Much effort is being made to identify brain …
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 …
A comprehensive report on machine learning-based early detection of alzheimer's disease using multi-modal neuroimaging data
S Sharma, PK Mandal - ACM Computing Surveys (CSUR), 2022 - dl.acm.org
Alzheimer's Disease (AD) is a devastating neurodegenerative brain disorder with no cure.
An early identification helps patients with AD sustain a normal living. We have outlined …
An early identification helps patients with AD sustain a normal living. We have outlined …
Machine learning roadmap for perovskite photovoltaics
M Srivastava, JM Howard, T Gong… - The Journal of …, 2021 - ACS Publications
Perovskite solar cells (PSC) are a favorable candidate for next-generation solar systems
with efficiencies comparable to Si photovoltaics, but their long-term stability must be proven …
with efficiencies comparable to Si photovoltaics, but their long-term stability must be proven …
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 …