Multi-Resolution Image Features of Retinal Images and Optic Nerve Head for Biomarker Identification in Attention-Deficit/Hyperactivity Disorder

Abstract

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by a persistent pattern of inattention and/or hyperactivity-impulsivity that interferes with functioning or development. Over the past 20 years, the number of teenagers diagnosed with ADHD has increased dramatically. This trend is not limited to the United States but is observed globally. Early diagnosis of ADHD is important because it allows for earlier treatment which can significantly improve symptoms and overall outcomes. Traditionally, diagnosing ADHD has relied heavily on self-checklists and observation from parents or teachers. However, these methods are often inaccurate, unscientific, and prone to subjective error. To solve this problem, in this research, I proposed a machine learning-based systematic ADHD diagnosis approach. The proposed system is developed with multi-resolution retinal image features and optic nerve head segmentation. Multi-resolution technique enables the model to learn from the input data in various perspectives. This results in rich feature extraction, in which the model gains deeper understanding and captures underlying patterns in the data. Optic nerve head segmentation enables the model to analyze one of the significant biomarkers of ADHD carefully in depth and have a synergistic effect in increasing the accuracy of the model when it is used with the multi-resolution technique. The experimental result of the proposed system achieved promising accuracy of 86.88% on ADHD retina image dataset.