Comparison of classifier strength for detection of retinal hemorrhages

ISSN: 22783075
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Abstract

Diabetes Mellitus(DM) which is the root cause of diabetic retinopathy(DR) diseases such as occlusion, microaneurysms, retinal hemorrhage, etc. Hemorrhage is considered the most dangerous among these, as it can accelerate the occurrence of vision loss. Hence, the severity of hemorrhages is analyzed in most of the recent studies of diabetic retinopathy detection. This paper focusses on the best classification approach by comparing different machine learning approach using supervised classifiers. Fundus image collected from publically available database are preprocessed and enhanced. Using splat based method, ground truth is established with the help of a retinal expert. Supervised classifiers are trained from the GLCM features extracted from the segmented images and validated on clinical images. The experimental results were verified by the Area Under Curve(AUC) for the three classifiers that were trained and results are verified and tabulated.

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Sreeja, K. A., & Kumar, S. S. (2019). Comparison of classifier strength for detection of retinal hemorrhages. International Journal of Innovative Technology and Exploring Engineering, 8(6), 688–693.

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