Diagnosis of Autism Spectrum Disorder Using Convolutional Neural Networks

Abstract

Autism spectrum disorder as a condition has posed significant early diagnosis challenges to the medical and health community for a long time. The early diagnosis of ASD is crucial for early intervention and adequate management of the condition. Several kinds of literature have shown that children with ASD have varying degrees of challenges in handwriting tasks; hence, this research has proposed the creation of a handwritten dataset of both ASD and non-ASD subjects for deep learning classification. The created dataset is based on a series of handwritten tasks given to subjects such as drawing and writing. The dataset was used to propose a deep learning automated ASD diagnosis method. Using the GoogleNet transfer learning algorithm, each handwritten task in the dataset is trained and classified for each subject. This is done because in real-life scenarios an ASD subject may not comply to performing and finishing all handwritten tasks. Using a training and testing ratio of 80:20, a total of 104 subjects’ handwritten tasks were used as input for training and classification, and it is shown that the proposed approach can correctly classify ASD with an accuracy of 90.48%, where sensitivity, specificity, and F1 score are calculated as 80%, 100%, and 100%, respectively. The results of our proposed method exhibit an impressive performance and indicate that the use of handwritten tasks has a significant potential for the early diagnosis of ASD.