Automated detection of ADHD: Current trends and future perspective

HW Loh, CP Ooi, PD Barua, EE Palmer… - Computers in Biology …, 2022 - Elsevier
Attention deficit hyperactivity disorder (ADHD) is a heterogenous disorder that has a
detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit …

Investigation of machine learning methods for early prediction of neurodevelopmental disorders in children

S Alam, P Raja, Y Gulzar - Wireless communications and …, 2022 - Wiley Online Library
Several variables, for instance, inheritance and surroundings, influence the growth of
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 …

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 …

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 …

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 …

Towards high-accuracy classifying attention-deficit/hyperactivity disorders using CNN-LSTM model

C Wang, X Wang, X **g, H Yokoi… - Journal of Neural …, 2022 - iopscience.iop.org
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 …

[HTML][HTML] Deep learning in pediatric neuroimaging

J Wang, J Wang, S Wang, Y Zhang - Displays, 2023 - Elsevier
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 …

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 …

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 …