<![CDATA[Background: Infant epilepsy is a serious neurological condition that is difficult to recognize visually because seizure manifestations often resemble normal infant reflexes. Specific background: Advances in computer vision and deep learning have enabled image-based seizure detection as a non-invasive approach for early identification in infants. Knowledge gap: Comparative evaluations of different YOLO model generations under identical experimental settings for infant seizure detection remain limited. Aims: This study evaluates and compares the performance of YOLOv8n, YOLOv10n, and YOLOv11n in detecting seizure activity from infant image data. Results: Using a dataset of 645 images across seizure and neutral classes, YOLOv11n achieved the highest precision, recall, and mean average precision at stricter localization thresholds, while YOLOv10n demonstrated the fastest inference time. Novelty: This study provides a systematic cross-generation evaluation of YOLO nano variants for infant seizure detection using uniform training parameters. Implications: The findings support informed selection of YOLO architectures for non-invasive, image-based early seizure detection systems in clinical and real-time monitoring contexts. Highlights • YOLOv11n shows the most stable detection performance for infant seizure images• YOLOv10n achieves the fastest inference time under identical training settings• Cross-version evaluation reveals clear trade-offs between detection stability and processing speed Keywords YOLO, Epilepsy, Computer Vision, Object Detection, Infant Seizure Detection]]>
Copyrights © 2026
