Journal of Psychology & Psychotherapy
Open Access

Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, India

Publications

• Short Communication   
  MVUT_GCN: Multi-View United Transformer Block-Graph Convolution Network for Diagnosis of Autism Spectrum Disorder
  Author(s): D. Darling Jemima*, Grace Selvarani and J. Daphy Louis Lovenia
  
  Identifying Autism Spectrum Disorder (ASD) is challenging due to its complex and varied nature, making early detection important for effective intervention. Recently, there has been considerable discussion about using deep learning algorithms to improve ASD diagnosis through neuroimaging data analysis. To address the limitations of current techniques, this research introduces an innovative approach called the Multi-View United Transformer Block of Graph Convolution Network (MVUT_GCN). MVUT_GCN leverages the benefits of multi-view learning and convolution processes to extract subtle patterns from structural and functional Magnetic Resonance Imaging (MRI) data. A comprehensive analysis using the Autism Brain Imaging Data Exchange (ABIDE) dataset demonstrates that MVUT_GCN outperforms the existing Multi View Site Graph Convolution Network (MVS_GCN), achieving a +3.44% improvement in accu.. View More»
  DOI: 10.35841/2161-0487.24.14.485

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