Journal Article

Diagnosis of diabetic retinopathy using multi level set segmentation algorithm with feature extraction using SVM with selective features

Kadry, Seifedine, Kandhasamy, J. Pradeep, Ramasamy, Lakshmana Kumar, Balamurali, S.

2020

Overview

Diabetic retinopathy is a major cause of blindness in diabetic patients. It is an eye disease caused by diabetes mellitus which affects the retina. Recognition of the severity of this disease at early stage is a challenging factor for the ophthalmologists. In this article, a novel diagnosis system for identifying the severity of diabetic retinopathy is proposed using a multi level set segmentation algorithm and support vector machine with selective features along with genetic algorithm. The proposed system uses some mathematical morphological operations for clustering. After that the clusters are passed to the multi level set segmentation algorithm and some features are extracted using Local Binary Patterns as a texture descriptor for retinal images, color moments and statistical features such as mean, median etc. to detect the major regions of retina. Then the extracted features are given to the support vector machine classifier to classify the disease severity. This system was evaluated and compared using measures of sensitivity and specificity. We obtain sensitivity of 97.14%, specificity of 100% and accuracy of 99.3% on an average. From the seen results, it is observed that our proposed system is suited for the diagnosis of diabetic retinopathy at the early stage.

• Publisher: Springer US,Springer Nature B.V
• Subject: Algorithms / Blindness / Clustering / Computer Communication Networks / Computer Science / Data Structures and Information Theory / Diabetes / Diabetes mellitus / Diabetic retinopathy / Diagnosis / Eye diseases / Feature extraction / Genetic algorithms / Image segmentation / Multimedia Information Systems / Retina / Retinal images / Sensitivity analysis / Special Purpose and Application-Based Systems / Support vector machines