Abstract
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.
Highlights
The World Health Organisation estimated that by 2030, there will be nearly 366 million people withDiabetic mellitus (DM) [1]
A microvascular complication of DM that is responsible for a major share of cases of blindness in the world is the Diabetic Retinopathy (DR)
Histogram equalization is done using Contrast limited Adaptive Histogram Equalization(c)[27]. Each image is normalized according to its prevailing pixel value at the three colour channels
Summary
The World Health Organisation estimated that by 2030, there will be nearly 366 million people withDiabetic mellitus (DM) [1]. A microvascular complication of DM that is responsible for a major share of cases of blindness in the world is the Diabetic Retinopathy (DR). The severe complications like Microaneurysms, Exudates, Occlusion, hemorrhages, etc., together known as DR. Retinal hemorrhages and other symptoms are usually diagnosed by an ophthalmoscope or a fundus camera that are capable to examine inside of eye. In order to reduce the diagnosing time, human error and increase the accuracy, several algorithms have been developed for the early detection of DR and all of them use machine learning techniques. The pre-processed fundus image features are extracted and applied to a supervised classifier which is trained with the relevant features by feature subset selection. Classification of hemorrhage and non-hemorrhage fundus images carried out using three different classifiers is presented. The techniques used to develop the algorithm is based on recent researches
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