ADHD classification using auto-encoding neural network and binary hypothesis testing
Abstract Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent
neurodevelopmental disease of school-age children. Early diagnosis is crucial for ADHD …
neurodevelopmental disease of school-age children. Early diagnosis is crucial for ADHD …
Retracted article: brain image classification by the combination of different wavelet transforms and support vector machine classification
SK Mishra, VH Deepthi - Journal of Ambient Intelligence and Humanized …, 2021 - Springer
The human brain is the primary organ, and it is located in the centre of the nervous system in
the human body. The abnormal cells in the brain are known as a brain tumor. The tumor in …
the human body. The abnormal cells in the brain are known as a brain tumor. The tumor in …
ADHD classification by dual subspace learning using resting-state functional connectivity
Y Chen, Y Tang, C Wang, X Liu, L Zhao… - Artificial intelligence in …, 2020 - Elsevier
As one of the most common neurobehavioral diseases in school-age children, Attention
Deficit Hyperactivity Disorder (ADHD) has been increasingly studied in recent years. But it is …
Deficit Hyperactivity Disorder (ADHD) has been increasingly studied in recent years. But it is …
Estimating sparse functional connectivity networks via hyperparameter-free learning model
Functional connectivity networks (FCNs) provide a potential way for understanding the brain
organizational patterns and diagnosing neurological diseases. Currently, researchers have …
organizational patterns and diagnosing neurological diseases. Currently, researchers have …
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 …
High-Accuracy Classification of Attention Deficit Hyperactivity Disorder With l2,1-Norm Linear Discriminant Analysis and Binary Hypothesis Testing
Y Tang, X Li, Y Chen, Y Zhong, A Jiang, C Wang - IEEE Access, 2020 - ieeexplore.ieee.org
Attention Deficit Hyperactivity Disorder (ADHD) is a high incidence of neurobehavioral
disease in school-age children. Its neurobiological diagnosis (or classification) is meaningful …
disease in school-age children. Its neurobiological diagnosis (or classification) is meaningful …
Stockwell transform of time-series of fMRI data for diagnoses of attention deficit hyperactive disorder
Attention deficit hyperactivity disorder (ADHD) is a common brain disorder among children. It
presents various symptoms, hence, utilizing the information obtained from functional …
presents various symptoms, hence, utilizing the information obtained from functional …
Graph kernel based clustering algorithm in MANETs
Y Song, H Luo, S Pi, C Gui, B Sun - IEEE Access, 2020 - ieeexplore.ieee.org
The mobile ad hoc network (MANET) is a kind of dynamic, easy to construct and universal
network, which has been widely concerned by a large number of researchers. Graph theory …
network, which has been widely concerned by a large number of researchers. Graph theory …
Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis
Abstract Attention Deficit Hyperactivity Disorder (ADHD) is characterized by deficits in
attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has …
attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has …
ADHD classification combining biomarker detection with attention auto-encoding neural network
Y Chen, Y Gao, A Jiang, Y Tang, C Wang - Biomedical Signal Processing …, 2023 - Elsevier
Abstract Attention Deficit Hyperactivity Disorder (ADHD) is one of most prevalent
neurodevelopmental disorders in children. In decades, various neurobiological diagnosis …
neurodevelopmental disorders in children. In decades, various neurobiological diagnosis …