{"title":"Enhancing diabetic retinopathy and macular edema detection through multi scale feature fusion using deep learning model.","authors":"Gowri L, Haris R, Sumathi M, S P Raja","doi":"10.1007/s00417-024-06687-4","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>This work tackles the growing problem of early identification of diabetic retinopathy and diabetic macular edema. The deep neural network design utilizes multi-scale feature fusion to improve automated diagnostic accuracy. Methods This approach uses convolutional neural networks (CNN) and is designed to combine higher-level semantic inputs with low-level textural characteristics. The contextual and localized abstract representations that complement each other are combined via a unique fusion technique.</p><p><strong>Results: </strong>Use the MESSIDOR dataset, which comprises retinal images labeled with pathological annotations, for model training and validation to ensure robust algorithm development. The suggested model shows a 98% general precision and good performance in diabetic retinopathy. This model achieves an impressive nearly 100% exactness for diabetic macular edema, with particularly high accuracy (0.99).</p><p><strong>Conclusion: </strong>Consistent performance increases the likelihood that the vision will be upheld through public screening and extensive clinical integration.</p>","PeriodicalId":12795,"journal":{"name":"Graefe’s Archive for Clinical and Experimental Ophthalmology","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Graefe’s Archive for Clinical and Experimental Ophthalmology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00417-024-06687-4","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: This work tackles the growing problem of early identification of diabetic retinopathy and diabetic macular edema. The deep neural network design utilizes multi-scale feature fusion to improve automated diagnostic accuracy. Methods This approach uses convolutional neural networks (CNN) and is designed to combine higher-level semantic inputs with low-level textural characteristics. The contextual and localized abstract representations that complement each other are combined via a unique fusion technique.
Results: Use the MESSIDOR dataset, which comprises retinal images labeled with pathological annotations, for model training and validation to ensure robust algorithm development. The suggested model shows a 98% general precision and good performance in diabetic retinopathy. This model achieves an impressive nearly 100% exactness for diabetic macular edema, with particularly high accuracy (0.99).
Conclusion: Consistent performance increases the likelihood that the vision will be upheld through public screening and extensive clinical integration.
期刊介绍:
Graefe''s Archive for Clinical and Experimental Ophthalmology is a distinguished international journal that presents original clinical reports and clini-cally relevant experimental studies. Founded in 1854 by Albrecht von Graefe to serve as a source of useful clinical information and a stimulus for discussion, the journal has published articles by leading ophthalmologists and vision research scientists for more than a century. With peer review by an international Editorial Board and prompt English-language publication, Graefe''s Archive provides rapid dissemination of clinical and clinically related experimental information.