2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 14 , Issue 2 , PP: 89-96, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Monalin Pal 1 * , Rubini P. 2
Doi: https://doi.org/10.54216/FPA.140207
Autism, a developmental and neurological disorder, impacts communication, interaction, and behavior, setting individuals with it apart from those without. This spectrum disorder affects various aspects of an individual's life, including social, cognitive, emotional, and physical health. Early detection and intervention are crucial for symptom reduction and facilitating learning and development. Recent advancements in machine learning and deep learning have facilitated the diagnosis of Autism by analyzing brain signals. This current study introduces an approach for Autism detection utilizing functional Magnetic Resonance Imaging (fMRI) data. The Autism Brain Imaging Data Exchange (ABIDE) dataset serves as the foundation, employing hierarchical graph pooling to abstract brain images into a graph structure. Graph Convolutional Networks are then used to learn node embeddings derived from sparse feature vectors. The model attains an accuracy of 87% on the 10-fold cross-validation dataset. This study proves to be cost-effective and efficient in identifying Autism through fMRI, making it suitable for near real-time applications.
Deep Learning , Machine Learning , Autism Spectrum Disorder , Speech Recognition , Fusion Processing , Information Fusion , Neural networks , Convolutional Neural Network , functional Magnetic Resonance Imaging (fMRI) , Autism Brain Imaging Data Exchange (ABIDE)
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