# Screening Autism Spectrum Disorder in children using Deep Learning Approach : Evaluating the classification model of YOLOv26s by comparing with other models

arXiv: https://arxiv.org/abs/2306.14300 · HTML: https://mlsanity.com/paper/2306.14300/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 25 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches](https://mlsanity.com/paper/1803.01164/) (2018)

## Abstract (arXiv)

Autism spectrum disorder (ASD) is a developmental condition that presents significant challenges in social interac- tion, communication, and behavior. Early intervention plays a pivotal role in enhancing cognitive abilities and reducing autistic symptoms in children with ASD. Numerous clinical studies have highlighted distinctive facial characteristics that distinguish ASD children from typically developing (TD) children. In this study, we propose a practical solution for ASD screening using facial images using YOLOv26s model. By employing YOLOv26s, a deep learning technique, we achieved exceptional results. Our model achieved a remarkable 92.86% accuracy in classification and an F1-score of 0.9291. Our findings provide support for the clini- cal observations regarding facial feature discrepancies between children with ASD. The high F1-score obtained demonstrates the potential of deep learning models in screening children with ASD. We conclude that the newest version of YOLOv26s which is usually used for object detection can be used for classification problem of Austistic and Non-autistic images.