A Brief Survey on Various Prediction Models for Detection of ADHD from Brain-MRI Images

Abstract

In recent years, we have experienced an exponentially rising attentiveness towards the application of various machine-learning models to delve into image-diagnosis and prediction of lesion changes in the neuro-radiology domain. There have been over 1000 publications in the last six years on subject classification focussing on various neuro-disorders, several of them based on Attention deficit hyperactivity disorder (ADHD). Elaborate reports on such studies, such as the machine learning models, specimen quantity, input feature category, and recorded accuracy, are abridged. The survey encapsulates evidence, standing constraints, and the study employing machine learning to diagnose neuro-disorders using MRI data. The major gridlock for this domain continues to be the sparse specimen pool. This challenge could be plausibly overcome by various latest data-sharing models.