Deep learning autism classification and prediction

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.