Mobile application for detecting autism spectrum disorder (ASD)

Lim, Chia Yoong (2025) Mobile application for detecting autism spectrum disorder (ASD). Final Year Project, UTAR.

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Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disability that affects how humans interact, communicate, and behave. Autistic people often find it hard to socialise with others and may engage in self-injurious behaviours. Specifically, there is no cure for this disorder. Additionally, it is expensive to detect ASD as it requires long-term monitoring by experts. Some diagnostic methods even involve brain scanning. Thus, it is unaffordable for most families, especially those with limited financial resources, even if their children suffer from this disorder. Early intervention is important, making early detection of ASD particularly significant. To address this challenge, face recognition technology powered by deep learning has emerged as a promising diagnostic tool. This project aims to implement transfer learning using facial recognition to detect ASD. While researchers have proven that questionnaires can be effective screening tools with good detection accuracy, this project seeks to further validate the accuracy of the detection process. The project provides two separate methods, which are facial detection and Q-Chat 10 approaches to streamline the ASD detection process.

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