Deep learning autism classification and prediction
- International Robotics & Automation Journal
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Sameer Hameed Abdulshahed,1
Ahmad Taha
Abdulsaddsa2
Abstract
One of the most prevalent illnesses in children is autism spectrum disorder (ASD) (1 in 44).
According to some estimates, 53% of kids with ASD engage in one or more challenging
behaviors (CB; aggression, self-injury, property destruction, elopement, etc.), which is
significantly higher than the prevalence among their peers who are typically developing
or who have other developmental disorders. Numerous, significant negative effects of CB
on the person exist, and they are linked to a worse long-term outlook. For caregivers of
children with ASD, the presence of CB is a better indicator of stress than the severity of the
child’s core ASD symptoms. The validity of fixed features extracted from autistic children’s
face photographs as a biomarker to demarcate them from healthy children is investigated in
this study paper. The proposed paper aims to use deep learning models (CNN) to classify
autism spectrum disorders based on facial expression images. By leveraging the power of
deep convolutional neural networks, based on the Kaggle dataset. We used and prepared
data input to CNN models where the split image in two parts horizontally and vertically as
feature extractor’s model as a binary classifier to identify autism in children accurately. Our
results reveal that the proposed model achieved an accuracy of 94%, Sensitivity of 93%
and Specificity of 95% this indicator is considered important and can be built or relied on.
Keywords
computer vision, machine learning, CNN, autism spectrum disorder, behavioral science
