A hybrid ensemble learning approach for efficient diabetic retinopathy prediction and classification using machine learning and deep learning techniques
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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ismail Saritas
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
AI 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.
Açıklama
Anahtar Kelimeler
Artificial Intelligence, Blood Sugar Levels, Diabetic Retinopathy, Early Detection, Transfer Learning, Visual Impairment
Kaynak
International Journal of Intelligent Systems and Applications in Engineering
WoS Q Değeri
Scopus Q Değeri
Q3
Cilt
12
Sayı
16s
Künye
Shah, 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.
