Diagnosis of Autism Spectrum Disorder Based on Symptoms and Face Recognition
DOI:
https://doi.org/10.51699/ijhsms.v2i10.2646Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that has various effects on language, speech, and communication of individuals. When ASD is detected in the earlier stages of life, especially in childhood, there would be many identifiers that would aid in the strategizing of the right therapeutic plan at the right time period.
Human faces have important markers that would aid in the identification of ASD through the analyzation of facial features and eye contact. There are other Artificial Intelligence-aided means of detection of ASD through the studying of various symptoms and finding patterns. In this research, we developed two systems that would aid in the diagnosis of Autism Spectrum Disorder in children, one of which uses a transfer-learning-based face detection framework. And the other system uses a decision-tree-based system to identify Autism in chlidren based on symptoms. Various machine learning and deep learning techniques were applied.
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