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Machine Learning for Diabetic Retinopathy Detection by Erukulla Amulya is a document available to read on EtoBox.

What is Machine Learning for Diabetic Retinopathy Detection about?

This research focuses on developing a machine learning model for the early diagnosis of diabetic retinopathy using fundus photographs, analyzing a substantial dataset of 14,402 images from multiple sources. The study implemented advanced feature engineering techniques and achieved a classification accuracy of 80.41% with the Random Forest algorithm. The findings highlight the potential for machine learning to enhance diagnostic processes in clinical settings, addressing the growing challenge of diabetes-rel

Author

Erukulla Amulya

Language

EN