1. Diabetic retinopathy using machine learning
Authors : Manisha Laxman Jadhav, M. Z. Shaikh
Pages : 35-41
DOI : http://dx.doi.org/10.21172/1.132.06
Keywords : diabetic retinopathy, Gabor, statistical features, SVM, Neural Network Abstract :Abstract— In the modern world, diabetic retinopathy (DR) has become one of the most severe complication prevalent among diabetic patients. The success rate of its curability solemnly depends on the early stage diagnosis or else will lead to total blindness. The paper proposes a novel method for the automated identification of diabetic retinopathy in fundus images based on machine learning technique. Approach employs a unique sequential execution of image enhancement, noise removal, blood vessels segmentation followed by optic disc elimination, exudates detection, microannynysms and hemorrhages’ detection to extract fundus image features like area of Microannenysms, exudates and hemorrhages, together with texture feature analysis using Gabor and statistical features. Finally features selected are passed into the well-known support vector machine (SVM) and neural network classifier which classifies the images into normal and abnormal classes. Real time and publicly available database analysis shows really encouraging performance metrics of the proposed method using neural network in terms of accuracy.
Citing this Journal Article :Manisha Laxman Jadhav, M. Z. Shaikh, "Diabetic retinopathy using machine learning", Volume 13 Issue 2 - April 2019, 35-41
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