Screening Autism Spectrum Disorder in children using Deep Learning Approach: Evaluating the classification model of YOLOv26s by comparing with other models
The episode reviews a study using deep learning to screen for Autism Spectrum Disorder (ASD) in children. The research focuses on evaluating the YOLOv26s model, which achieved 89.64% accuracy using a substantial Kaggle dataset. By comparing it against other models and finding that the AdamW optimizer was most effective, this work suggests a reliable and cost-efficient method for early diagnosis.