Retinal Image Analysis for Simultaneous Classification and Severity Grading of Attention-Deficit Hyperactivity Disorder and Autism Spectrum Disorder using Deep Learning

Over the last 20 years, the number of children born with Autism Spectrum Disorder (ASD) and Attention-Deficit Hyperactivity Disorder (ADHD) has significantly increased. According to the U.S Centers for Disease Control and Prevention, the number of children with ASD in the United States had increased from 1 in 150 in 2000 to 1 in 36 in 2020, which is an increase of 316.7% and the number of children with ADHD has been around 10% of Population. As the number of children with ASD and ADHD is increasing rapidly, an early diagnosis of both mental disorders is essential, which can contribute to the improvement of the condition of children significantly. By employing retinal images, the early detection of ASD and ADHD in the developmental stages of childhood becomes feasible. Consequently, the development of a diagnostic system that identifies the presence of ASD and ADHD is essential for healthcare interventions and the enhancement of children's well-being when potential remediation is attainable. This research aims to develop a system for early diagnosis of ASD and ADHD utilizing retinal images and deep learning through convolutional neural networks. The proposed approach classifies the retinal images into disorder categories and severity levels. Experimental results demonstrate the viability of the proposed approach as a biometric for the early diagnosis of ASD and ADHD.