A Convolutional Neural Network Model for Early-Stage Detection of Autism Spectrum Disorder

Autism is a developmental handicap of children that gets worse as they age. An autistic child has problems with interaction and communication, as well as limited behavior. If autistic children are diagnosed early, they can have a quality life by providing thorough care and therapy. However, in many developed countries, it is too late to diagnose children with autism. Besides, a trained medical expert is required to identify autism as there are no direct medical tests. Medical practitioners also take enough time to detect it because the children have to be monitored intensively. In this research, artificial intelligence algorithms have been utilized for detecting autism in children from images that are not viable for ordinary people. We have employed five different algorithms that are Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting Machine (GBM), AdaBoost (AB) and Convolutional Neural Network (CNN) for classifying Autism Spectrum Disorder (ASD) in children. Comparing classification performances among those algorithms, we have achieved the highest accuracy of 92.31 % on CNN, which outperformed the other conventional Machine Learning (ML) algorithms. Therefore, we proposed a prediction model based on CNN, which can be used for detecting ASD, especially for children.