Empowering healthcare innovation: IoT-enabled smart systems and deep learning for enhanced diabetic retinopathy in the telehealth landscape

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dc.contributor.authorShah, Arpit
dc.contributor.authorPatel, Warish
dc.contributor.authorKoyuncu, Hakan
dc.date.accessioned2024-04-04T12:14:27Z
dc.date.available2024-04-04T12:14:27Z
dc.date.issued2024en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractBackground: Among prevalent medical complications, Diabetic Eye Disease (DED) stands as a significant contributor to vision loss. To forecast its progression and accurately assess the various stages, diverse methodologies have emerged. Machine Learning (ML) and Deep Learning (DL) algorithms have become essential tools in this endeavor, primarily through their adept analysis of Diabetic Retinopathy (DR) images. However, there is still a need for a more efficient and accurate method to predict DR performance. Method: We have developed an innovative method for classifying and predicting diabetic retinopathy. The novel idea in this research is to combine several techniques, including ensemble learning and a 2D convolutional neural network; we utilized transfer learning and a correlation method in our approach. Initially, the Stochastic Gradient Boosting process was employed for predicting diabetic retinopathy. We then used a boosting-based Ensemble Learning method for predicting images of diabetic retinopathy. Next, we applied a 2D Convolutional Neural Network. We successfully employed Transfer Learning to classify different stages of diabetic retinopathy images accurately. This research explores the role of artificial intelligence in identifying and categorizing diabetic retinopathy at an early stage, using techniques such as machine learning and deep learning. It also use techniques like transfer learning, domain adaptation, multitask learning, and explainable AI to accurately classify different stages of diabetic retinopathy images. Our proposed technique achieves impressive results through experiments, with a 97.9% accuracy in forecasting DR images and a 98.1% accuracy in image grading. Additionally, sensitivity and specificity metrics measure 99.3% and 97.6%, respectively. Comparative analysis with existing methods underscores the high predictive accuracy achieved by our proposed approach.en_US
dc.identifier.citationShah, A., Patel, W., Koyuncu, H. (2024). Empowering healthcare innovation: IoT-enabled smart systems and deep learning for enhanced diabetic retinopathy in the telehealth landscape. Journal of Interdisciplinary Mathematics, 27(2), 355-367. 10.47974/JIM-1836en_US
dc.identifier.endpage367en_US
dc.identifier.issn0972-0502
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage355en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4667
dc.identifier.volume27en_US
dc.identifier.wosWOS:001202268700017
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKoyuncu, Hakan
dc.language.isoen
dc.publisherTaru Publicationsen_US
dc.relation.ispartofJournal of Interdisciplinary Mathematics
dc.relation.isversionof10.47974/JIM-1836en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCross-domain knowledge transferen_US
dc.subjectDiabetic retinopathy prediction and classificationen_US
dc.subjectExplainable AIen_US
dc.subjectHybrid ensemble learning and 2D convolutional neural networksen_US
dc.subjectManaging diabetic eye diseaseen_US
dc.subjectRetinal fundus mage analysisen_US
dc.titleEmpowering healthcare innovation: IoT-enabled smart systems and deep learning for enhanced diabetic retinopathy in the telehealth landscape
dc.typeArticle

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