A Hybrid Ensemble Learning Approach for Efficient Diabetic Retinopathy Prediction and Classification Using Machine Learning and Deep Learning Techniques

Authors

  • Arpit Shah Computer Engineering Department, Parul University, Vadodara, Gujarat
  • Warish Patel Computer Engineering Department, Parul University, Vadodara, Gujarat
  • Hakan Koyuncu Computer Engineering Department, Altinbas University, Istanbul, Turkey

Keywords:

Diabetic Retinopathy, Blood Sugar Levels, Visual Impairment, Artificial Intelligence, Early Detection, Transfer Learning

Abstract

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.

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Published

23.02.2024

How to Cite

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. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4793

Issue

Vol. 12 No. 16s (2024)

Section

Research Article

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