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A possible option made available by recent advancements in algorithms, particularly in the fields of AI and ML, is automating the detection and classification of diabetic retinopathy (DR). This work aims to improve diagnostic efficacy and accuracy by exploring the application of ensemble learning methodologies to develop robust DR detection algorithms. The study enhances existing efforts to develop machine learning-based classifiers for early DR prediction by leveraging structural predictors and complex computational models. The essay reviews the literature on automated DR identification and highlights the importance of ensemble learning in addressing the pressing need for diagnosis of people with diabetes.

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Enhanced Diabetic Retinopathy Detection Mechanism Through Ensemble Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Kapil Chaturvedi,
  • Vijay Bhandari,
  • Anil Kothari,
  • Ritu Shrivastava,
  • Rishabh Tiwari

摘要

A possible option made available by recent advancements in algorithms, particularly in the fields of AI and ML, is automating the detection and classification of diabetic retinopathy (DR). This work aims to improve diagnostic efficacy and accuracy by exploring the application of ensemble learning methodologies to develop robust DR detection algorithms. The study enhances existing efforts to develop machine learning-based classifiers for early DR prediction by leveraging structural predictors and complex computational models. The essay reviews the literature on automated DR identification and highlights the importance of ensemble learning in addressing the pressing need for diagnosis of people with diabetes.