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Improved Detection Accuracy of Red Lesions in Retinal Fundus Images with Superlearning Approach by Moumita Sahoo; Santanu Ghorai; Madhuchhanda Mitra; Saurabh Pal is a Medicine article available to read on EtoBox.

What is Improved Detection Accuracy of Red Lesions in Retinal Fundus Images with Superlearning Approach about?

Background: Diabetic Retinopathy (DR) is a serious consequence of diabetes that can result to permanent vision loss for a person. Diabetes-related vision impairment can be significantly avoided with timely screening and treatment in its initial phase. The earliest and the most noticeable indications on the surface of the retina are micro-aneurysm and haemorrhage, which appear as dark patches. Therefore, the automatic detection of retinopathy begins with the identification of all these dark lesions. Method: In our study, we have developed a clinical knowledge based segmentation built on Early Treatment DR Study (ETDRS). ETDRS is a gold standard for identifying all red lesions using adaptive-thresholding approach followed by different pre-processing steps. The lesions are classified using super-learning approach to improve multi-class detection accuracy. Ensemble based super-learning approach finds optimal weights of base learners by minimizing the cross validated risk-function and it pledges the improved performance compared to baselearners predictions. For multi-class classification, a well informative feature-set based on colour, intensity, shape, size and texture, is developed. I

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Author

Moumita Sahoo; Santanu Ghorai; Madhuchhanda Mitra; Saurabh Pal

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Health Sciences)