Few-Shot Learning for Early Diagnosis of Autism Spectrum Disorder in Children

Abstract

Autism Spectrum Disorder (ASD) is a developmental condition affecting communication, behavior, and social interactions, characterized by a range of symptoms and severity levels where early detection and intervention are crucial for effective support. Acquiring labeled data is challenging due to high costs, specialized knowledge, or sample scarcity, especially in rare conditions like childhood ASD. Accordingly, we utilized few-shot learning (FSL) which is a machine-learning technique that trains the models by meta-learning with a minimal amount of labeled data, often just a few examples for each class. We developed an advanced FSL model that achieved a 95% accuracy rate in classifying an image-based dataset for childhood autism. This paper discusses the potential benefits and limitations of the proposed FSL model for ASD early detection and provides suaaestions for future research in this domain.