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SpermYOLO AI model enhances sperm and impurity detection in microscopy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SpermYOLO, a new AI model derived from YOLOv11, designed for the precise detection of sperm and impurities in microscopic images. This framework incorporates several architectural enhancements, including C3k2-IDB for feature extraction, D2SEM for semantic enhancement, MFM for feature fusion, and the DESD Head for prediction. Tested on the SVIA semen microscopic imaging benchmark, SpermYOLO achieved high accuracy for both sperm and impurity detection, outperforming existing detectors while maintaining a lightweight model size. The model also demonstrated effectiveness on the SDTB testicular-biopsy benchmark, handling small sperm targets and complex backgrounds. AI

IMPACT This model offers improved accuracy and efficiency for sperm detection in medical imaging, potentially aiding in assisted reproductive technologies.

RANK_REASON The cluster describes a new AI model presented in a research paper on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SpermYOLO AI model enhances sperm and impurity detection in microscopy

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The cluster describes a new AI model presented in a research paper on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shengqi Chen, Zilin Wang, Xingyu Pan, Wenting Yu, Pengchao Deng, Guohua Wu ·

    SpermYOLO: A Coordinated YOLO-Based Detector for Accurate and Efficient Sperm and Impurity Detection in Microscopic Images

    arXiv:2609.14278v1 Announce Type: cross Abstract: Accurate sperm detection is essential for computer-assisted semen analysis, yet it remains challenging in microscopic images due to dense distributions, visually similar artifacts, and sperm-like impurities. In this paper, we prop…

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