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Research Article: Detection of ASD Children through Deep-Learning Application of fMRI

Date Published: 2023-10-05

Abstract:
Autism spectrum disorder (ASD) necessitates prompt diagnostic scrutiny to enable immediate, targeted interventions. This study unveils an advanced convolutional-neural-network (CNN) algorithm that was meticulously engineered to examine resting-state functional magnetic resonance imaging (fMRI) for early ASD detection in pediatric cohorts. The CNN architecture amalgamates convolutional, pooling, batch-normalization, dropout, and fully connected layers, optimized for high-dimensional data interpretation. Rigorous preprocessing yielded 22,176 two-dimensional echo planar samples from 126 subjects (56 ASD, 70 controls) who were sourced from the Autism Brain Imaging Data Exchange (ABIDE I) repository. The model, trained on 17,740 samples across 50 epochs, demonstrated unparalleled diagnostic metrics—accuracy of 99.39%, recall of 98.80%, precision of 99.85%, and an F1 score of 99.32%—and thereby eclipsed extant computational methodologies. Feature map analyses substantiated the model’s hierarchical feature extraction capabilities. This research elucidates a deep learning framework for computer-assisted ASD screening via fMRI, with transformative implications for early diagnosis and intervention.

Introduction:
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is characterized not only by impairments in social interaction and communication but also by restricted, repetitive patterns of behavior, interests, or activities, as outlined in DSM-5 criteria [1,2]. The imperative for early diagnostic endeavors is crucial for the initiation of targeted therapeutic interventions. However, traditional diagnostic paradigms hinge largely on subjective evaluations, often resulting in protracted and…

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