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The document presents a supervised approach for the automated detection of retinal hemorrhages in fundus images, focusing on diabetic retinopathy (DR) which can lead to blindness. The proposed algorithm achieved a sensitivity of 90.42% and specificity of 93.53% by utilizing morphological operations and a random forest classifier on a dataset of 4546 blobs from 50 images. This automated system aims to assist ophthalmologists in early diagnosis and treatment of DR, addressing the challenges of manual detectio

Author

abc

Language

EN