A Multi-Modal Approach for Early Autism Recognition Using Deep Learning and Sensory Data

Abstract

With Early detection of Autism Spectrum Disorder (ASD) can make a life-changing difference in a child’s journey, helping them receive the right support at the right time. This project introduces a smart, hybrid system that combines advanced deep learning technology with proven treatment methods, aiming to close the gap between diagnosis and meaningful help. It examines various types of information such as behavioural assessments and sensory response patterns to train a model that can identify early signs of autism with high accuracy and consistency. When the system detects a possible case, it provides structured, theory-based activities designed to develop cognitive, social, emotional, and communication skills in young children. These activities are based on widely accepted approaches and are intended to encourage steady developmental growth. A major strength of this system is its automated reporting feature, which gathers diagnostic insights, structured treatment recommendations, and predicted progress into a clear, easy-to-read report for parents, therapists, and healthcare professionals, ensuring everyone stays informed and aligned. By blending advanced computational analysis with trusted treatment practices, the system ensures both accurate detection and a smooth path to intervention. It supports early diagnosis, ongoing guidance, and continuous monitoring, helping reduce delays and improving engagement in a child’s developmental plan. This combined approach demonstrates how technology and professional expertise can work together to create an accessible, practical tool for ASD management. Its goal is to transform early detection into immediate, meaningful action that nurtures potential, builds confidence, and helps shape a brighter future for every child.