Classification of age-related macular degeneration using very deep learning neural network based on transfer learning

Classification of age-related macular degeneration using very deep learning neural network based on transfer learning | 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 Classification of age-related macular degeneration using very deep learning neural network based on transfer learning Ngoc Thien Le, Truong Thanh Le, Rath Itthipanichpong, Pear Ferreira Pongsachareonnont, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2294957/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 : Detecting and classifying the age-related macular degeneration (AMD) at the elderly people is one of the main concerns of the national public health program in Thailand. Within this research purview, accurate and sensitive performance is highly desirable, and a variety of proposed models have been developed toward this end. Deep learning neural networks have recently been shown to have advantage over existing approaches for the task of classifying the level of eye disease. Despite of these advances, there is still significant potential for development, regarding model classification accuracy, and sensitive values. Results : Six very deep learning neural networks (DLNN), named as InceptionV3, ResNet152V2, DenseNet201, EfficientNetB7, InceptionResNetV2, and NASNetLarge are proposed for training to detect and classify the AMD disease from fundus images. The training process with AMD images is implemented based on the transfer learning technique through Google Colab Pro platform. The experiments showed that, for 3-classes AMD classification (Normal, Dry AMD, and Wet AMD), the overall classification accuracy of DenseNet201 is highest and is about 88% with the testing dataset. Furthermore, for the inferred 2-class AMD classification (Normal vs. AMD), the most accuracy values obtained from EfficientNetB7 and InceptionResNetV2 about 93.5% and 95.06%, respectively. Conclusions : The experimental results have shown that, after applying transfer learning technique, the DenseNet201 model had higher accuracy performance in 3-class AMD classification of fundus images than other very deep learning neural networks and other state-of-the-art proposed deep learning models in the literature. Furthermore, the trained EfficientNetB7 and InceptionResNetV2 showed the advantages for the 2-class AMD classification over other existing models. Age-related macular degeneration (AMD) Fundus images Deep learning neural network Transfer learning 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. 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-2294957","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":157982328,"identity":"f47c59ae-d3a4-4ba9-ae06-e6ea19b3a71c","order_by":0,"name":"Ngoc Thien Le","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ngoc","middleName":"Thien","lastName":"Le","suffix":""},{"id":157982329,"identity":"56e9991f-cc0a-4f7a-9644-b8a487d07ee7","order_by":1,"name":"Truong Thanh Le","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Truong","middleName":"Thanh","lastName":"Le","suffix":""},{"id":157982330,"identity":"b5d4b0f6-7f31-4ff2-94a9-fccf1913c2e5","order_by":2,"name":"Rath Itthipanichpong","email":"","orcid":"","institution":"King Chulalongkorn Memorial Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rath","middleName":"","lastName":"Itthipanichpong","suffix":""},{"id":157982331,"identity":"c32c3787-d07b-46f5-a24b-d953a0b75c17","order_by":3,"name":"Pear Ferreira Pongsachareonnont","email":"","orcid":"","institution":"King Chulalongkorn Memorial Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pear","middleName":"Ferreira","lastName":"Pongsachareonnont","suffix":""},{"id":157982333,"identity":"45a49795-ead3-43c0-9533-17a2bbce0ee2","order_by":4,"name":"Apivat Mavichak","email":"","orcid":"","institution":"King Chulalongkorn Memorial Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Apivat","middleName":"","lastName":"Mavichak","suffix":""},{"id":157982335,"identity":"f7ac68c3-3cbc-4664-b64c-fa712c91431a","order_by":5,"name":"Disorn Suwajanakorn","email":"","orcid":"","institution":"King Chulalongkorn Memorial Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Disorn","middleName":"","lastName":"Suwajanakorn","suffix":""},{"id":157982336,"identity":"d630c139-dd73-4cef-82d3-f73b566482af","order_by":6,"name":"Watit Benjapolakul","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYJCCA0AkxyDBwHgAwuchpIGZ4cABhgPGQC0MEC1sRGgBKj2Q2EC0FoPj5w8e/sBwJ71/do/BgR8MdvIM8r0H8Gs5kwxy2LPcGXfOGBzsYUg2bGDjS8CrxewAWMvh3A0SOQYHeBiYE4AOM8Cv5fxjsJZ0A6CWg38Y6onQcgNiSwJIy2EeIIOgFvsbjw0OnDF4ZjjjRlrBYRmD44ZtbDn4tUj2Jz7+UFFxR55/RvLGh28qquX5mc/g1wIBBkgMNiLUj4JRMApGwSggAACO50kyYcps9gAAAABJRU5ErkJggg==","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Watit","middleName":"","lastName":"Benjapolakul","suffix":""}],"badges":[],"createdAt":"2022-11-21 02:29:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2294957/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2294957/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":31176887,"identity":"cab03312-0631-4350-a0d1-d9a7a5a426c7","added_by":"auto","created_at":"2023-01-05 17:44:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2329629,"visible":true,"origin":"","legend":"","description":"","filename":"BMCBioinformatics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2294957/v1_covered.pdf"},{"id":30057106,"identity":"e4d692f2-f644-432d-9979-fc597febb0c9","added_by":"auto","created_at":"2022-12-08 10:38:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2363367,"visible":true,"origin":"","legend":"","description":"","filename":"BMCBioinformatics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2294957/v1/4baf46211dc12937b67ddd62.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Classification of age-related macular degeneration using very deep learning neural network based on transfer learning","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":"Age-related macular degeneration (AMD), Fundus images, Deep learning neural network, Transfer learning","lastPublishedDoi":"10.21203/rs.3.rs-2294957/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2294957/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Detecting and classifying the age-related macular degeneration (AMD) at the elderly people is one of the main concerns of the national public health program in Thailand. Within this research purview, accurate and sensitive performance is highly desirable, and a variety of proposed models have been developed toward this end. Deep learning neural networks have recently been shown to have advantage over existing approaches for the task of classifying the level of eye disease. Despite of these advances, there is still significant potential for development, regarding model classification accuracy, and sensitive values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Six very deep learning neural networks (DLNN), named as InceptionV3, ResNet152V2, DenseNet201, EfficientNetB7, InceptionResNetV2, and NASNetLarge are proposed for training to detect and classify the AMD disease from fundus images. The training process with AMD images is implemented based on the transfer learning technique through Google Colab Pro platform. The experiments showed that, for 3-classes AMD classification (Normal, Dry AMD, and Wet AMD), the overall classification accuracy of DenseNet201 is highest and is about 88% with the testing dataset. Furthermore, for the inferred 2-class AMD classification (Normal vs. AMD), the most accuracy values obtained from EfficientNetB7 and InceptionResNetV2 about 93.5% and 95.06%, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The experimental results have shown that, after applying transfer learning technique, the DenseNet201 model had higher accuracy performance in 3-class AMD classification of fundus images than other very deep learning neural networks and other state-of-the-art proposed deep learning models in the literature. Furthermore, the trained EfficientNetB7 and InceptionResNetV2 showed the advantages for the 2-class AMD classification over other existing models.\u003c/p\u003e","manuscriptTitle":"Classification of age-related macular degeneration using very deep learning neural network based on transfer learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-08 10:38:27","doi":"10.21203/rs.3.rs-2294957/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":"337000df-a07c-409f-a7e5-955d83907aa1","owner":[],"postedDate":"December 8th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-01-05T17:44:28+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-08 10:38:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2294957","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2294957","identity":"rs-2294957","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}