Automatic diagnosis of laryngeal diseases and segmentation of laryngeal cancer based on deep learning technology

Automatic diagnosis of laryngeal diseases and segmentation of laryngeal cancer based on deep learning technology | 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 Automatic diagnosis of laryngeal diseases and segmentation of laryngeal cancer based on deep learning technology Xiaohong Jiang, Lili Chen, Xiang Wang, Xianwen Wu, Nannan Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7764053/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Laryngeal cancer is currently a common clinical malignancy, and its early diagnosis is of great significance for improving patient survival rates. With the widespread application of deep learning technology in the field of medical image analysis, this study aims to establish an intelligent diagnostic system for early screening and precise diagnosis of laryngeal cancer. Methods We design the ScSE and GC modules and develop a lightweight laryngeal disease classification network named LLLDNet, employing a combined enhancement strategy of transfer learning and knowledge distillation to improve the accuracy of laryngeal disease image classification. we also design the HA and MSFF modules to develop a laryngeal cancer segmentation network named HAM-Unet, which integrates hybrid attention and multi-scale feature fusion. Finally, we conduct experiments on the proposed LLLDNet classification network and HAM-Unet segmentation network using datasets of laryngeal diseases and laryngeal cancer, respectively. Results Experimental results demonstrate that LLLDNet achieves an accuracy of 93.0% and a recall of 92.4%. While improving various metrics including accuracy, LLLDNet reduces the number of model parameters to only 0.425M and FLOPs to 294M, achieving sufficient lightweight design, conserving computational resources, and accelerating inference. The HAM-Unet network attains an IOU of 90.86% and an accuracy of 93.92%, significantly outperforming other segmentation models and enabling more precise delineation of laryngeal cancer lesion regions. Diagnosis of laryngeal diseases lightweight network knowledge distillation multi-scale feature fusion Laryngeal Cancer Segmentation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-7764053","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588464916,"identity":"1cae57ef-783a-454b-addb-e6159debc583","order_by":0,"name":"Xiaohong Jiang","email":"","orcid":"","institution":"Chongqing jiaotong university","correspondingAuthor":false,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Jiang","suffix":""},{"id":588464917,"identity":"c20b6503-454f-48c2-bf39-ac290b758c0e","order_by":1,"name":"Lili Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3RsQqCQBjA8TsObJFcL4R6hS8EJx/mjqA5CKLNi0DHVnsLIYjGE4eWo9kxHyFcGoI6jaBJHYPuv5wc3w/uQ4RMpl9sgDeSP4Kxo7+huZFdhGAhuZh7I6GJ7Ef0DBY5T+Vnuos4WyJkeSL4cM7T5S1C42HBcLVoITSvH6Ys4qv5ArIIeaOCETdpIVDvwizb8gsbasLTglnE7ibU9pI3CfsRHgEF+iYMukizC1MMaL2LutDpXpVbt404cVyW9/Uz3MX5EdarYDI8z7KqjXzn679P9YlFT4CQd+09ajKZTH/VCx8bUD29EWtxAAAAAElFTkSuQmCC","orcid":"","institution":"Chongqing jiaotong university","correspondingAuthor":true,"prefix":"","firstName":"Lili","middleName":"","lastName":"Chen","suffix":""},{"id":588464918,"identity":"cb92fc79-4371-4198-8b22-3be6e7287792","order_by":2,"name":"Xiang Wang","email":"","orcid":"","institution":"Chongqing jiaotong university","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Wang","suffix":""},{"id":588464919,"identity":"433f07b4-b20e-40bf-9c01-a655395a9fd9","order_by":3,"name":"Xianwen Wu","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xianwen","middleName":"","lastName":"Wu","suffix":""},{"id":588464920,"identity":"d1241f62-06d2-4e97-b683-b8b9a1ed4743","order_by":4,"name":"Nannan Zhang","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Nannan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-10-02 06:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7764053/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7764053/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103398153,"identity":"74736d33-732e-4657-b0d3-3b6cb2e2c118","added_by":"auto","created_at":"2026-02-25 08:59:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1252955,"visible":true,"origin":"","legend":"","description":"","filename":"Automaticdiagnosisoflaryngealdiseasesandsegmentationoflaryngealcancerbasedondeeplearningtechnology.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7764053/v1_covered_d97c7445-7324-4b8a-b7db-db6bb5579a06.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Automatic diagnosis of laryngeal diseases and segmentation of laryngeal cancer based on deep learning technology","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diagnosis of laryngeal diseases, lightweight network, knowledge distillation, multi-scale feature fusion, Laryngeal Cancer Segmentation","lastPublishedDoi":"10.21203/rs.3.rs-7764053/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7764053/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLaryngeal cancer is currently a common clinical malignancy, and its early diagnosis is of great significance for improving patient survival rates. With the widespread application of deep learning technology in the field of medical image analysis, this study aims to establish an intelligent diagnostic system for early screening and precise diagnosis of laryngeal cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe design the ScSE and GC modules and develop a lightweight laryngeal disease classification network named LLLDNet, employing a combined enhancement strategy of transfer learning and knowledge distillation to improve the accuracy of laryngeal disease image classification. we also design the HA and MSFF modules to develop a laryngeal cancer segmentation network named HAM-Unet, which integrates hybrid attention and multi-scale feature fusion. Finally, we conduct experiments on the proposed LLLDNet classification network and HAM-Unet segmentation network using datasets of laryngeal diseases and laryngeal cancer, respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eExperimental results demonstrate that LLLDNet achieves an accuracy of 93.0% and a recall of 92.4%. While improving various metrics including accuracy, LLLDNet reduces the number of model parameters to only 0.425M and FLOPs to 294M, achieving sufficient lightweight design, conserving computational resources, and accelerating inference. The HAM-Unet network attains an IOU of 90.86% and an accuracy of 93.92%, significantly outperforming other segmentation models and enabling more precise delineation of laryngeal cancer lesion regions.\u003c/p\u003e","manuscriptTitle":"Automatic diagnosis of laryngeal diseases and segmentation of laryngeal cancer based on deep learning technology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 17:45:12","doi":"10.21203/rs.3.rs-7764053/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"98c4ce0c-b164-4052-aa8c-78c6d5c30c91","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-25T08:57:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 17:45:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7764053","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7764053","identity":"rs-7764053","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}