Precise Detection of Eimeria Oocysts in Sheep: A Deep Learning Model Based on Microscopic Images

Precise Detection of Eimeria Oocysts in Sheep: A Deep Learning Model Based on Microscopic Images | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Method Article Precise Detection of Eimeria Oocysts in Sheep: A Deep Learning Model Based on Microscopic Images Liangliang Liu, Jinpu Xie, Huikai Qin, Xiangqing Sui, Longxian Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6670314/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Nov, 2025 Read the published version in Parasites & Vectors → Version 1 posted 10 You are reading this latest preprint version Abstract Background Parasitic infections remain a significant contributor to productivity losses in global livestock production systems. Conventional diagnostic approaches rely on specialized microscopic equipment and veterinary pathology expertise. Contemporary automated detection systems face implementation barriers stemming from scarcity of annotated microscopy datasets and persistent challenges in discriminative feature extraction. Methods This study develops YOLO-GA, a deep learning-enhanced object detection framework for precise identification of Eimeria oocysts in ovine specimens. Building upon the YOLOv5 architecture, we integrate two novel components: 1) Contextual Transformer (CoT) modules for localized contextual feature enhancement, and 2) Normalized Attention Mechanisms (NAM) for adaptive feature recalibration. Architectural optimizations including skip connections and hierarchical feature fusion were incorporated into the Backbone and Neck networks. Results Experimental validation using a curated dataset of 1,500 Eimeria oocyst microscopy images (200× optical magnification) demonstrated model efficacy, achieving a mean average precision (mAP) of 98.9% with 95.2% precision. Comparative analyses revealed YOLO-GA's superior performance across evaluation metrics while maintaining a computationally efficient framework relative to existing detection models. Conclusions The YOLO-GA framework successfully achieves automated Eimeria oocyst detection with high diagnostic accuracy and operational robustness. This advancement establishes a foundation for intelligent veterinary parasitology diagnostics and scalable livestock health monitoring solutions. Eimeria infection parasite oocysts deep learning object detection YOLOv5 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Full Text Additional Declarations No competing interests reported. Table 1 is available in the Supplementary Files section. Supplementary Files Table1.docx Table 1 Comparison results of state-of-the-art detection models. Cite Share Download PDF Status: Published Journal Publication published 12 Nov, 2025 Read the published version in Parasites & Vectors → Version 1 posted Editorial decision: Revision requested 18 Jun, 2025 Reviews received at journal 11 Jun, 2025 Reviewers agreed at journal 26 May, 2025 Reviews received at journal 24 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers invited by journal 20 May, 2025 Editor assigned by journal 19 May, 2025 Submission checks completed at journal 19 May, 2025 First submitted to journal 15 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6670314","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":459253147,"identity":"4d47f18c-6640-409f-9f43-e93051bfbbb6","order_by":0,"name":"Liangliang 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(a) Sample quantity and class distribution in the training set; (b) Size and count distribution of bounding boxes; (c) Spatial distribution of bounding box centers within the entire image; (d) Aspect ratio distribution of targets relative to the image dimensions.\u003c/p\u003e","description":"","filename":"Fig.10.png","url":"https://assets-eu.researchsquare.com/files/rs-6670314/v1/84b191e74f9dce5c7f61bff8.png"},{"id":83420370,"identity":"9eede16f-0062-4b91-b5bb-b670f7a4d08f","added_by":"auto","created_at":"2025-05-26 01:48:39","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":272689,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix of annotation features.This correlogram illustrates the pairwise relationships between key bounding box attributes such as center coordinates, width, and height, revealing the statistical dependencies in object shape and spatial distribution.\u003c/p\u003e","description":"","filename":"Fig.11.png","url":"https://assets-eu.researchsquare.com/files/rs-6670314/v1/3d2048c1aa7475cae226a27b.png"},{"id":83420371,"identity":"a7fbf850-3418-4644-8a40-77cea9b64ac2","added_by":"auto","created_at":"2025-05-26 01:48:39","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1878544,"visible":true,"origin":"","legend":"\u003cp\u003eComparative visualization of \u003cem\u003eEimeria\u003c/em\u003e oocyst detection under varying conditions: (A) Single target in complex background (B) Multi-target in simple background (C) Occluded targets (D) Multi-target in cluttered environment. 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Conventional diagnostic approaches rely on specialized microscopic equipment and veterinary pathology expertise. Contemporary automated detection systems face implementation barriers stemming from scarcity of annotated microscopy datasets and persistent challenges in discriminative feature extraction.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study develops YOLO-GA, a deep learning-enhanced object detection framework for precise identification of \u003cem\u003eEimeria\u003c/em\u003e oocysts in ovine specimens. Building upon the YOLOv5 architecture, we integrate two novel components: 1) Contextual Transformer (CoT) modules for localized contextual feature enhancement, and 2) Normalized Attention Mechanisms (NAM) for adaptive feature recalibration. Architectural optimizations including skip connections and hierarchical feature fusion were incorporated into the Backbone and Neck networks.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eExperimental validation using a curated dataset of 1,500 \u003cem\u003eEimeria\u003c/em\u003e oocyst microscopy images (200\u0026times; optical magnification) demonstrated model efficacy, achieving a mean average precision (mAP) of 98.9% with 95.2% precision. Comparative analyses revealed YOLO-GA's superior performance across evaluation metrics while maintaining a computationally efficient framework relative to existing detection models.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe YOLO-GA framework successfully achieves automated \u003cem\u003eEimeria\u003c/em\u003e oocyst detection with high diagnostic accuracy and operational robustness. This advancement establishes a foundation for intelligent veterinary parasitology diagnostics and scalable livestock health monitoring solutions.\u003c/p\u003e","manuscriptTitle":"Precise Detection of Eimeria Oocysts in Sheep: A Deep Learning Model Based on Microscopic Images","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-26 01:48:31","doi":"10.21203/rs.3.rs-6670314/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-18T18:21:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-11T10:08:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114864835530817506365101012972579473296","date":"2025-05-26T04:22:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-24T14:42:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91475958717081825366482753068386757865","date":"2025-05-20T10:18:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326737025183966544919409384159587227748","date":"2025-05-20T09:57:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-20T09:12:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-19T07:54:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-19T05:18:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Parasites \u0026 Vectors","date":"2025-05-15T08:01:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"parasites-and-vectors","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"parv","sideBox":"Learn more about [Parasites \u0026 Vectors](http://parasitesandvectors.biomedcentral.com/)","snPcode":"13071","submissionUrl":"https://submission.nature.com/new-submission/13071/3","title":"Parasites \u0026 Vectors","twitterHandle":"@bugbittentweets","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"32323c72-770c-43e1-8716-df544316f3c5","owner":[],"postedDate":"May 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:10:52+00:00","versionOfRecord":{"articleIdentity":"rs-6670314","link":"https://doi.org/10.1186/s13071-025-07092-4","journal":{"identity":"parasites-and-vectors","isVorOnly":false,"title":"Parasites \u0026 Vectors"},"publishedOn":"2025-11-12 15:58:20","publishedOnDateReadable":"November 12th, 2025"},"versionCreatedAt":"2025-05-26 01:48:31","video":"","vorDoi":"10.1186/s13071-025-07092-4","vorDoiUrl":"https://doi.org/10.1186/s13071-025-07092-4","workflowStages":[]},"version":"v1","identity":"rs-6670314","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6670314","identity":"rs-6670314","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}