Multimodel Autism Spectrum Disorder Diagnosis Model Based on Deep GCN

Author(s)	Kummetha Deepthi, K. Bhanu Prakash
Country	India
Abstract	Integrating a variety of data is necessary for diagnosing Autism Spectrum Disorder (ASD). assessments of behavior, scans of the brain using neuroimaging, and genetic markers. A novel multimodal diagnosis model that is based on Deep Diagram Convolutional Organizations (Deep GCN). Each data type is processed by the model. separately, locating relevant features, and building a single graph representation that captures intermodal complex relationships. Deep GCN Following that, layers learn hierarchical representations by iteratively aggregating and fusing information to improve the accuracy of diagnostics by utilizing the insights that work together the proposed model uses behavioral, neuroimaging, and genetic data to provide a diagnosis framework for ASD that is both comprehensive and interpretable. Experiments used for validation show that the model works well for integrating multimodal data and enhancing diagnostic capabilities, making available promising headways in clinical choice emotionally supportive networks for chemical imbalance finding.
Keywords	Deep Learning, Autism Spectrum Disorder (ASD), Deep Diagram Convolutional Organizations (Deep GCN).
Field	Engineering
Published In	Volume 7, Issue 6, June 2026
Published On	2026-06-04

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