Screening Autism Spectrum Disorder in childrens using Deep Learning
  Approach : Evaluating the classification model of YOLOv8 by comparing with
  other models
Subash Gautam Prabin Sharma Kisan Thapa Mala Deep Upadhaya Dikshya Thapa Salik Ram Khanal Vítor Manuel de Jesus Filipe
Autism spectrum disorder (ASD) is a developmental condition that presents
significant challenges in social interaction, 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 YoloV8 model. By employing
YoloV8, a deep learning technique, on a dataset of Kaggle, we achieved
exceptional results. Our model achieved a remarkable 89.64% accuracy in
classification and an F1-score of 0.89. Our findings provide support for the
clinical 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 YoloV8 which is usually used for object detection can be used for
classification problem of Austistic and Non-autistic images.