Framework for Cerebral Palsy Detection in Automated Diagnosis System Using Deep Learning Algorithm

This research paper introduces a comprehensive approach to early cerebral palsy detection in toddlers, integrating game-based assessments, neuroimaging with a deep learning algorithm, audio technology for dysarthria symptom identification, and an Automated Response Intrusion Detection System (ARIDS) for app security. By leveraging toddlers' natural inclination for enjoyable activities, the game-based assessment tool captures subtle cues indicative of cerebral palsy. Neuroimaging techniques categorize disabilities, and the deep learning algorithm classifies cases and quantifies brain damage. Audio technology tracks dysarthria symptoms, offering a proactive means of intervention. The ARIDS ensures app security. This multifaceted framework aims to transform early detection, potentially revolutionizing pediatric healthcare and improving outcomes for toddlers with cerebral palsy.