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Traditional microscopic diagnostic methods are labor-intensive and require specialized veterinary expertise. Recent automated detection systems are hindered by limited annotated microscopy datasets and the difficulty of extracting discriminative features from small, overlapping targets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We propose YOLO-GA, an enhanced object detection framework, for accurate identification of\n                      <jats:italic>Eimeria<\/jats:italic>\n                      oocysts in ovine microscopy images. Built upon the YOLOv5\u2019s architecture, the model incorporates two lightweight attention modules: (1) Contextual Transformer (CoT) blocks for local\u2013global contextual enhancement and (2) Normalized Attention Mechanisms (NAM) for adaptive feature recalibration. The proposed model is optimized for both accuracy and computational efficiency.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experiments on a curated dataset of 2000 microscopy images (200\u00d7\u2009magnification) demonstrated that YOLO-GA achieves a mean (\u00b1 standard deviation) average precision (mAP@0.5) of 98.9%\u2009\u00b1\u20090.1, with 95.2%\u2009\u00b1\u20090.3 precision and real-time inference speed. Comparative evaluations against recent detectors, including YOLOv8, YOLOv10 and DETR variants, confirmed the superior performance of YOLO-GA across multiple runs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      YOLO-GA offers a high-accuracy solution with balanced computational efficiency for automated detection of\n                      <jats:italic>Eimeria<\/jats:italic>\n                      oocysts under complex microscopy conditions. This work lays a foundation for intelligent diagnostics of ovine\n                      <jats:italic>Eimeria<\/jats:italic>\n                      coccidiosis and provides a reference for scalable health monitoring of sheep flocks, with potential extension to other small ruminant coccidiosis (e.g. goat\n                      <jats:italic>Eimeria<\/jats:italic>\n                      ) pending further validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1186\/s13071-025-07092-4","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:29:43Z","timestamp":1762957783000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Precise detection of Eimeria oocysts in sheep: a deep learning model based on microscopic images"],"prefix":"10.1186","volume":"18","author":[{"given":"Liangliang","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinpu","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huikai","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangqing","family":"Sui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longxian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,12]]},"reference":[{"key":"7092_CR1","unstructured":"Kaufmann J. 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