Hamza Osman Ilhan

Abstract Accurate and reliable analysis of sperm motility is critical for assessing male fertility and improving treatment outcomes in assisted reproductive technologies. In this study, we propose an automated sperm motility analysis framework that integrates the YOLOv12n object detector, the BoostTrack++ multi-object tracker, and a novel Density-Adaptive Tracklet Stitching (DATS) post-tracking refinement module.

Abstract Artificial intelligence (AI), particularly large language models (LLMs), is an increasingly prominent tool in medical and dental education. Trained with deep learning and NLP techniques, these models interpret meaning, generate text, and manage complex information. They hold potential for practical educational applications, such as supporting exam preparation and personalized learning.

Unveiling the capabilities of vision transformers in sperm morphology analysis: a comparative evaluation 1Department of Computer Engineering Faculty of Technology, Marmara University Istanbul, Istanbul, Turkey 2Department of Computer Engineering Faculty of Electrical and Electronics, Yildiz Technical University, İstanbul, Turkey 3Department of Biomedical Engineering Faculty of Electrical and Electronics, Yildiz Technical University, İstanbul, Turkey DOI 10.7717/peerj-cs.3173 Academic Editor...