Automated detection of ADHD: Current trends and future perspective
Attention deficit hyperactivity disorder (ADHD) is a heterogenous disorder that has a
detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit …
detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit …
Investigation of machine learning methods for early prediction of neurodevelopmental disorders in children
Several variables, for instance, inheritance and surroundings, influence the growth of
neurodevelopmental disorders, eg, autism spectrum disorder (ASD) and attention deficit …
neurodevelopmental disorders, eg, autism spectrum disorder (ASD) and attention deficit …
[HTML][HTML] Insight into ADHD diagnosis with deep learning on Actimetry: Quantitative interpretation of occlusion maps in age and gender subgroups
P Amado-Caballero, P Casaseca-de-la-Higuera… - Artificial Intelligence in …, 2023 - Elsevier
Abstract Attention Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental
disorder in childhood that often persists into adulthood. Objectively diagnosing ADHD can …
disorder in childhood that often persists into adulthood. Objectively diagnosing ADHD can …
Alzheimer's disease prediction using machine learning techniques and principal component analysis (PCA)
M Sudharsan, G Thailambal - Materials Today: Proceedings, 2023 - Elsevier
Alzheimer's disease (AD) is a neurodegenerative disease of the human brain that affects
neurotransmitters, tissue, and neurons that impair the senses, memories, and behaviors …
neurotransmitters, tissue, and neurons that impair the senses, memories, and behaviors …
Deep learning models for digital image processing: a review
R Archana, PSE Jeevaraj - Artificial Intelligence Review, 2024 - Springer
Within the domain of image processing, a wide array of methodologies is dedicated to tasks
including denoising, enhancement, segmentation, feature extraction, and classification …
including denoising, enhancement, segmentation, feature extraction, and classification …
Deep learning and multimodal feature fusion for the aided diagnosis of Alzheimer's disease
H Jia, H Lao - Neural Computing and Applications, 2022 - Springer
The accurate diagnosis of Alzheimer's disease (AD) in the early stages, such as significant
memory concern (SMC) and mild cognitive impairment (MCI), is essential in order to slow its …
memory concern (SMC) and mild cognitive impairment (MCI), is essential in order to slow its …
Towards high-accuracy classifying attention-deficit/hyperactivity disorders using CNN-LSTM model
Objective. The neurocognitive attention functions involve the cooperation of multiple brain
regions, and the defects in the cooperation will lead to attention-deficit/hyperactivity disorder …
regions, and the defects in the cooperation will lead to attention-deficit/hyperactivity disorder …
[HTML][HTML] Deep learning in pediatric neuroimaging
The integration of deep learning techniques in pediatric neuroimaging has shown significant
promise in advancing various aspects of the field. This paper provides a comprehensive …
promise in advancing various aspects of the field. This paper provides a comprehensive …
Comparative study of various machine learning methods on ASD classification
R Rimal, M Brannon, Y Wang, X Yang - International Journal of Data …, 2023 - Springer
The autism dataset is studied to identify the differences between autistic and healthy groups.
For this, the resting-state functional magnetic resonance imaging data of the two groups are …
For this, the resting-state functional magnetic resonance imaging data of the two groups are …
A review of visual sustained attention: neural mechanisms and computational models
H Huang, R Li, J Zhang - PeerJ, 2023 - peerj.com
Sustained attention is one of the basic abilities of humans to maintain concentration on
relevant information while ignoring irrelevant information over extended periods. The …
relevant information while ignoring irrelevant information over extended periods. The …