Automated Detection of Soil transmitted Helminthes and Schistosomiasis Using YOLO Based Deep Learning Model

Automated Detection of Soil transmitted Helminthes and Schistosomiasis Using YOLO Based Deep Learning Model | 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 Research Article Automated Detection of Soil transmitted Helminthes and Schistosomiasis Using YOLO Based Deep Learning Model Abrham Adamu, Etefa Belachew, Kris Calpotura, Aregawi Gebremedhin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8887946/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 1 You are reading this latest preprint version Abstract Soil-transmitted helminths (STHs) and schistosomiasis remain prevalent public health concerns in tropical and resource constrained regions necessitating accurate and timely diagnostic approaches for effective treatment and control. However, conventional microscopic examination of parasite eggs is laborious, time-intensive and dependent on expert interpretation which can introduce variability and diagnostic errors. To address these challenges, this study proposes a real time deep learning-based detection and classification using YOLO-based object detection models for automated analysis of microscopy images. Specifically, YOLOv11 and YOLOv12 architectures were evaluated across three lightweights to medium model variants (Nano, Small and Medium) to assess tradeoffs between detection accuracy, inference speed and computational efficiency. A custom annotated dataset comprising 1713 images from Ethiopian health institute with four clinically relevant parasite classes (Ascaris, hookworm, Schistosomia and Trichuris) was used for independent training, validation, and testing. Model performance was evaluated using standard object detection metrics including mean Average Precision at IoU 0.5 ( [email protected] , precision, recall and F1-score) and inference speed. A 5-fold cross validation and statistical significance analysis (p < 0.001) were conducted to ensure robustness and reproducibility. Experimental results indicate that YOLOv12m achieves the highest detection performance ( [email protected] = 94%) and [email protected] –0.9 of 67.4%, while yolo11m scored the highest recall of 88.6% indicating fewer false Negative. In contrast, YOLOv12n offers the lowest computational cost 6.3 GFLOPs by delivering the fast inference of 159FPS with reduced sensitivity. These findings demonstrate the effectiveness of YOLO-based models for scalable, real time parasite detection and provide practical guidance for deployment in resource-constrained healthcare settings. Soil transmitted helminths and schistosomiasis Yolo Object detection parasite egg detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted First submitted to journal 15 Feb, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8887946","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592105716,"identity":"f5b6dbeb-f56e-4334-b17f-d97ed5917898","order_by":0,"name":"Abrham Adamu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYDACCQY2EMXYACQO/6gAkszMDURrYXzMcAakhZF4LczGjG1wG3ED/tm9zx78zLGT7Z92xky6cF5tNH87UMuPim24Lblz3Nywd1uy8YzbOWbSM7cdz51xmLGBsefMbdzW3Ehjk+DdxpzYANQCZBzLbQBqYWZsw61FHqhF8u+2+sT5YC1zjuXOJ6TFAKhFmnfb4cQNt3OMjXkbanI3ENJieOcYm7TstuPGG2+nFT6ccexA7kagloP4/CJ3u41N8u22atl5t5M3HPhQU5c77/zhgw9+VODxPho4DCYPEK0eCOpIUTwKRsEoGAUjBAAAgFtgvXGQDFgAAAAASUVORK5CYII=","orcid":"","institution":"Haramaya University","correspondingAuthor":true,"prefix":"","firstName":"Abrham","middleName":"","lastName":"Adamu","suffix":""},{"id":592105717,"identity":"28dea025-2fe5-4c09-965c-acd9c69490fa","order_by":1,"name":"Etefa Belachew","email":"","orcid":"","institution":"Jimma University – Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Etefa","middleName":"","lastName":"Belachew","suffix":""},{"id":592105718,"identity":"aed51cde-3170-4db1-bde2-0ac7c4470302","order_by":2,"name":"Kris Calpotura","email":"","orcid":"","institution":"Jimma University – Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Kris","middleName":"","lastName":"Calpotura","suffix":""},{"id":592105719,"identity":"911a768c-6e1f-44d3-9b78-979d5ed40a50","order_by":3,"name":"Aregawi Gebremedhin","email":"","orcid":"","institution":"Mekelle University, Ethiopian Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Aregawi","middleName":"","lastName":"Gebremedhin","suffix":""}],"badges":[],"createdAt":"2026-02-15 18:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8887946/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8887946/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102963304,"identity":"b71401bf-a3fa-4892-9449-695df7d9b43e","added_by":"auto","created_at":"2026-02-19 04:15:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1022391,"visible":true,"origin":"","legend":"","description":"","filename":"AutomatedDetectionofSTHandSchistosomiausingyolodeeplearningEditedandFinaltopublish.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8887946/v1_covered_8ad644b6-a043-4697-81bf-335174da3a12.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Automated Detection of Soil transmitted Helminthes and Schistosomiasis Using YOLO Based Deep Learning Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"parasitology-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pare","sideBox":"Learn more about [Parasitology Research](http://link.springer.com/journal/436)","snPcode":"436","submissionUrl":"https://submission.nature.com/new-submission/436/3","title":"Parasitology Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Soil transmitted helminths and schistosomiasis, Yolo, Object detection, parasite egg detection","lastPublishedDoi":"10.21203/rs.3.rs-8887946/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8887946/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil-transmitted helminths (STHs) and schistosomiasis remain prevalent public health concerns in tropical and resource constrained regions necessitating accurate and timely diagnostic approaches for effective treatment and control. However, conventional microscopic examination of parasite eggs is laborious, time-intensive and dependent on expert interpretation which can introduce variability and diagnostic errors. To address these challenges, this study proposes a real time deep learning-based detection and classification using YOLO-based object detection models for automated analysis of microscopy images. Specifically, YOLOv11 and YOLOv12 architectures were evaluated across three lightweights to medium model variants (Nano, Small and Medium) to assess tradeoffs between detection accuracy, inference speed and computational efficiency. A custom annotated dataset comprising 1713 images from Ethiopian health institute with four clinically relevant parasite classes (Ascaris, hookworm, Schistosomia and Trichuris) was used for independent training, validation, and testing. Model performance was evaluated using standard object detection metrics including mean Average Precision at IoU 0.5 ([email protected], precision, recall and F1-score) and inference speed. A 5-fold cross validation and statistical significance analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were conducted to ensure robustness and reproducibility. Experimental results indicate that YOLOv12m achieves the highest detection performance ([email protected]\u0026thinsp;=\u0026thinsp;94%) and [email protected]\u0026ndash;0.9 of 67.4%, while yolo11m scored the highest recall of 88.6% indicating fewer false Negative. In contrast, YOLOv12n offers the lowest computational cost 6.3 GFLOPs by delivering the fast inference of 159FPS with reduced sensitivity. These findings demonstrate the effectiveness of YOLO-based models for scalable, real time parasite detection and provide practical guidance for deployment in resource-constrained healthcare settings.\u003c/p\u003e","manuscriptTitle":"Automated Detection of Soil transmitted Helminthes and Schistosomiasis Using YOLO Based Deep Learning Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 08:36:50","doi":"10.21203/rs.3.rs-8887946/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"submitted","content":"Parasitology Research","date":"2026-02-15T18:50:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"parasitology-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pare","sideBox":"Learn more about [Parasitology Research](http://link.springer.com/journal/436)","snPcode":"436","submissionUrl":"https://submission.nature.com/new-submission/436/3","title":"Parasitology Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"225086bf-92c0-4c09-9abd-e3109cc2504e","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T11:24:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 08:36:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8887946","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8887946","identity":"rs-8887946","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}