Smart Autism Prediction Using Deep Learning and Image Processing

• Author(s): Snega B.; A.S. Arunachalam
• Paper ID: 1717448
• Page: 789-797
• Published Date: 08-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

This project presents a smart system for predicting autism spectrum disorder (ASD) using deep learning techniques, particularly Convolutional Neural Networks (CNN). The system analyzes facial images to identify patterns that may be associated with autism. Users can upload an image, which is then processed and classified into either autistic or non-autistic categories. The model evaluates its performance using metrics such as accuracy, precision, recall, and F1-score. The backend is implemented using Python along with deep learning libraries, and results are stored for further analysis. The proposed system aims to support early detection of autism, which can help in providing timely intervention and improving overall outcomes. This system helps in early diagnosis of autism, enabling timely intervention and improved healthcare outcomes.

Keywords

CNN, Autism Spectrum Disorder (ASD), CGRNN, Image Processing.

Citations

IRE Journals:
Snega B., A.S. Arunachalam "Smart Autism Prediction Using Deep Learning and Image Processing" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 789-797 https://doi.org/10.64388/IREV9I11-1717448

IEEE:
Snega B., A.S. Arunachalam "Smart Autism Prediction Using Deep Learning and Image Processing" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717448