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 …

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

RA Bahathiq, H Banjar, AK Bamaga… - Frontiers in …, 2022 - frontiersin.org
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects
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

Y Chen, J Yan, M Jiang, T Zhang… - … on Neural Networks …, 2022 - ieeexplore.ieee.org
Graph neural networks (GNNs) have received increasing interest in the medical imaging
field given their powerful graph embedding ability to characterize the non-Euclidean …

Is attention all you need in medical image analysis? A review

G Papanastasiou, N Dikaios, J Huang… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
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 …

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 …

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 …

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 …

Rapidly detecting fennel origin of the near-infrared spectroscopy based on extreme learning machine

E Zuo, L Sun, J Yan, C Chen, C Chen, X Lv - Scientific reports, 2022 - nature.com
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 …

[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 …

Multi-modal non-euclidean brain network analysis with community detection and convolutional autoencoder

Q Zhu, J Yang, S Wang, D Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …