Automated Detection of Retinal Hemorrhage based on Supervised Classifiers

K.A Sreeja, S.S Kumar


Supervised machine learning algorithm based retinal hemorrhage detection and classification is presented. For developing an automated diabetic retinopathy screening system, efficient detection of retinal hemorrhage is important. Splat, which is a high level entity in image segmentation is used to mark out hemorrhage in the pre-processed fundus image. Here, color images of retina are portioned into different segments (splats) covereing the whole image. With the help of splat level and GLCM features extracted from the splats, three classifiers are trained and tested using the relevant features. The ground-truth is established with the help of a retinal expert and using dataset and clinical images the validation was done. The output obtained using the three classifiers had more than 96 % sensitivity and accuracy.


Retinal hemorrhage; Diabetic retinopathy; Fundus image; Splat feature classification; GLCM features

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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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