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Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review
J Chaki, M Woźniak - Biomedical Signal Processing and Control, 2023 - Elsevier
A neurodegenerative disorder, such as Parkinson's, Alzheimer's, epilepsy, stroke, and
others, is a type of disease in which central nervous system cells stop working or die …
others, is a type of disease in which central nervous system cells stop working or die …
Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects
approximately 1% of the population and causes significant burdens. ASD's pathogenesis …
approximately 1% of the population and causes significant burdens. ASD's pathogenesis …
Adversarial learning based node-edge graph attention networks for autism spectrum disorder identification
Graph neural networks (GNNs) have received increasing interest in the medical imaging
field given their powerful graph embedding ability to characterize the non-Euclidean …
field given their powerful graph embedding ability to characterize the non-Euclidean …
Is attention all you need in medical image analysis? A review
Medical imaging is a key component in clinical diagnosis, treatment planning and clinical
trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance …
trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance …
Do it the transformer way: A comprehensive review of brain and vision transformers for autism spectrum disorder diagnosis and classification
AG Alharthi, SM Alzahrani - Computers in Biology and Medicine, 2023 - Elsevier
Autism spectrum disorder (ASD) is a condition observed in children who display abnormal
patterns of interaction, behavior, and communication with others. Despite extensive research …
patterns of interaction, behavior, and communication with others. Despite extensive research …
Evaluation of risk of bias in neuroimaging-based artificial intelligence models for psychiatric diagnosis: a systematic review
Z Chen, X Liu, Q Yang, YJ Wang, K Miao… - JAMA Network …, 2023 - jamanetwork.com
Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …
A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder
M Wang, Z Ma, Y Wang, J Liu, J Guo - Plos one, 2023 - journals.plos.org
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose current
psychiatric diagnostic process is subjective and behavior-based. In contrast, functional …
psychiatric diagnostic process is subjective and behavior-based. In contrast, functional …
Rapidly detecting fennel origin of the near-infrared spectroscopy based on extreme learning machine
Fennel contains many antioxidant and antibacterial substances, and it has very important
applications in food flavoring and other fields. The kinds and contents of chemical …
applications in food flavoring and other fields. The kinds and contents of chemical …
[HTML][HTML] Multi-slice generation sMRI and fMRI for autism spectrum disorder diagnosis using 3D-CNN and vision transformers
AG Alharthi, SM Alzahrani - Brain Sciences, 2023 - mdpi.com
Researchers have explored various potential indicators of ASD, including changes in brain
structure and activity, genetics, and immune system abnormalities, but no definitive indicator …
structure and activity, genetics, and immune system abnormalities, but no definitive indicator …
Multi-modal non-euclidean brain network analysis with community detection and convolutional autoencoder
Brain network analysis is one of the most effective methods for brain disease diagnosis.
Existing studies have shown that exploring information from multimodal data is a valuable …
Existing studies have shown that exploring information from multimodal data is a valuable …