The diagnosis of ASD with MRI: a systematic review and meta-analysis

SJC Schielen, J Pilmeyer, AP Aldenkamp… - Translational …, 2024 - nature.com
While diagnosing autism spectrum disorder (ASD) based on an objective test is desired, the
current diagnostic practice involves observation-based criteria. This study is a systematic …

Algorithmic approaches to classify autism spectrum disorders: a research perspective

SG Jacob, MMBA Sulaiman, B Bennet - Procedia Computer Science, 2022 - Elsevier
Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental disability that exhibits
sluggish progress in vocal development, restricted interest in normal activity and repetitive …

Autism spectrum disorder classification using Adam war strategy optimization enabled deep belief network

V Bhandage, S Muppidi, B Maram - Biomedical Signal Processing and …, 2023 - Elsevier
Autism spectrum disorder (ASD) is a brain disorder caused by dysfunction in the brain. ASD
patients have social interaction and communication problems that are determined by …

Automatic diagnosis of autism spectrum disorder detection using a hybrid feature selection model with graph convolution network

MR Lamani, PJ Benadit - SN Computer Science, 2023 - Springer
A neurodevelopmental disorder is called an autism spectrum disorder (ASD) that influences
a person's assertion, interaction, and learning abilities. The consequences and severity of …

Autism spectrum disorder detection using brain MRI image enabled deep learning with hybrid sewing training optimization

V Prasad, GV Sriramakrishnan… - Signal, Image and Video …, 2023 - Springer
Autism spectrum disorder (ASD) is brain enabled disorder representing behaviors in a
repetitive manner and social deficits. In this paper, ASD is diagnosed using brain magnetic …

Identification of autism spectrum disorder with functional graph discriminative network

J Li, F Wang, J Pan, Z Wen - Frontiers in Neuroscience, 2021 - frontiersin.org
Autism spectrum disorder (ASD) is a specific brain disease that causes communication
impairments and restricted interests. Functional connectivity analysis methodology is widely …

Brainsteam: A practical pipeline for connectome-based fmri analysis towards subject classification

A Li, Y Yang, H Cui, C Yang - PACIFIC SYMPOSIUM ON …, 2023 - World Scientific
Functional brain networks represent dynamic and complex interactions among anatomical
regions of interest (ROIs), providing crucial clinical insights for neural pattern discovery and …

基于时变特性的多层脑网络拓扑属性 分析及脑疾病分类.

**涛, 邱震钰, **瑶, **囡, **埼钒… - Science Technology & …, 2023 - search.ebscohost.com
摘要多层脑功能网络已经广泛应用于疾病的诊断. 现有研究大多利用动态功能连接的改变诊断
疾病, 极少探索多层网络拓扑属性对疾病分类的影响. 目前主要通过计算所有单层网络拓扑属性 …

Spatio-Temporal Attention in Multi-Granular Brain Chronnectomes For Detection of Autism Spectrum Disorder

J Orme-Rogers, A Srivastava - ICASSP 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
The traditional methods for detecting autism spectrum disorder (ASD) are expensive,
subjective, and time-consuming, often taking years for a diagnosis, with many children …

[HTML][HTML] A Survey on Genetic Disease− Autism Spectrum Disorder Prediction and Classification in Machine Learning

A Kanchana, R Khilar - International Journal of Nutrition …, 2024 - journals.lww.com
Autism spectrum disorder (ASD) is a hereditary, neurological condition with many
aetiologies that manifest in early childhood. Mental illnesses, including anxiety, poor …