A hybrid ensemble learning approach for efficient diabetic retinopathy prediction and classification using machine learning and deep learning techniques

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dc.contributor.authorShah, Arpit
dc.contributor.authorPatel, Warish
dc.contributor.authorKoyuncu, Hakan
dc.date.accessioned2024-03-23T08:52:52Z
dc.date.available2024-03-23T08:52:52Z
dc.date.issued2024en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractAI is a crucial tool in early detection and classification of diabetic retinopathy, which is a leading cause of visual impairment globally. Transfer Learning (TL) was used to improve the accuracy of predictions and classifications within training datasets, surpassing existing methodologies. The study provides comprehensive insights into current databases, screening programs, performance evaluation metrics, relevant biomarkers, and challenges encountered in ophthalmology. The findings underscore the potential of AI-based approaches in enhancing diagnostic precision and offer a promising direction for future studies. The paper concludes by delineating opportunities for further research and development in integrating AI advancements in the field. Conclusion: The findings underscore the efficacy of Transfer Learning in significantly improving the accuracy of diabetic retinopathy image predictions. This research highlights the potential of AI-based approaches in enhancing diagnostic precision and offers a promising direction for future studies. The paper concludes by delineating opportunities for further research and development, emphasizing the continued integration of advanced AI methodologies in ophthalmology to advance diabetic retinopathy detection and management.en_US
dc.identifier.citationShah, A., Patel, W., Koyuncu, H. (2024). A hybrid ensemble learning approach for efficient diabetic retinopathy prediction and classification using machine learning and deep learning techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 85-93.en_US
dc.identifier.endpage93en_US
dc.identifier.issn2147-6799
dc.identifier.issue16sen_US
dc.identifier.scopus2-s2.0-85185940582
dc.identifier.scopusqualityQ3
dc.identifier.startpage85en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4642
dc.identifier.volume12en_US
dc.indekslendigikaynakScopus
dc.institutionauthorKoyuncu, Hakan
dc.language.isoen
dc.publisherIsmail Saritasen_US
dc.relation.ispartofInternational Journal of Intelligent Systems and Applications in Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectBlood Sugar Levelsen_US
dc.subjectDiabetic Retinopathyen_US
dc.subjectEarly Detectionen_US
dc.subjectTransfer Learningen_US
dc.subjectVisual Impairmenten_US
dc.titleA hybrid ensemble learning approach for efficient diabetic retinopathy prediction and classification using machine learning and deep learning techniques
dc.typeArticle

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