Deep learning algorithms for autism spectrum disorder detection using eye-tracking patterns

Early detection of autism spectrum disorder through hybrid deep learning and classical machine learning approaches

These findings demonstrate the potential of computer vision-based analysis of children’s expressive activities as an effective, non-invasive ASD pre-screening tool and will focus on expanding dataset diversity and integrating multimodal behavioral cues to improve model generalization and clinical applicability.

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

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.

Data‐Driven Approaches for Autism Detection: A Comprehensive Review of Machine Learning Algorithms and Datasets

The need to develop large, well‐balanced datasets, the application of explainable AI techniques, standardization and regulatory guidelines for facilitating the clinical translation of ASD detection systems are suggested.

Advanced Computational Approaches for Early detection of Autism Spectrum Disorder using Machine and Deep Learning: Recent Trends and Perspective

Autism Spectrum Disorder (ASD) is a neurological and developmental condition characterized by challenges in social interaction, communication (both verbal and non-verbal), and repetitive behaviours. While genetics play a key role in its onset, early diagnosis remains essential for effective intervention. Machine learning (ML) offers a promising approach to streamline and accelerate ASD detection, making it faster and more cost-effective than traditional methods. This paper evaluates eight classification models to identify key ASD features and automate diagnosis. We compare their performance on large datasets to enhance predictive accuracy. ML has transformed healthcare by leveraging vast data volumes for analysis, with technological advances over the past decade improving diagnostic tools now standard in medical settings. ASD affects individuals variably, with symptoms typically appearing between 18 months and 3 years. Although genetic and environmental factors contribute, no single cause is confirmed. Traditional screenings rely heavily on clinician expertise, involving manual assessments and scoring, which can be subjective and time-consuming—even experts face uncertainties in predicting onset or severity. Parents seek rapid, reliable results. ML and deep learning (DL) address these gaps by analyzing complex patterns in data, enabling early prediction of ASD and its severity. This study implements diverse algorithms to support precise, automated screening, reducing diagnostic delays and improving outcomes.

Early Detection of

Attention-Driven Deep Learning Approach for Autism Spectrum Disorder Detection using Eye-Tracking Scanpath Representation

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that manifests itself through unusual social interactions and visual attention. Thus, it is of prime importance to diagnose the condition correctly. In this context, this paper proposes a deep learning-based framework for the detection of Autism Spectrum Disorder by utilizing eye-tracking images. The structured framework of the paper is as follows: the images are first preprocessed through a structured framework involving resizing, min-max normalization, and Otsu threshold-based segmentation. Then, rotation-based data augmentation is performed on the images. Finally, the EfficientNet-B4 network is used in combination with Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) for the detection of ASD. The experimental results of the framework have shown that the framework has achieved a classification accuracy of 98.1%, thereby outperforming than other state-of-the-art techniques.