Diabetic Retinopathy (DR) is an eye disease, which may cause blindness by the upsurge of
insulin in blood. The major cause of visual loss in diabetic patient is macular edema. To diagnose and
follow up Diabetic Macular Edema (DME), a powerful Optical Coherence Tomography (OCT) technique
is used for the clinical assessment. Many existing methods found out the DME affected patients
by estimating the fovea thickness. These methods have the issues of lower accuracy and higher time
complexity. In order to overwhelm the above limitations, a hybrid approaches based DR detection is
introduced in the proposed work. At first, the input image is preprocessed using green channel extraction and median filter.
Subsequently, the features are extracted by gradient-based features like Histogram of Oriented Gradient (HOG) with
Complete Local Binary Pattern (CLBP). The texture features are concentrated with various rotations to calculate the
edges. We present a hybrid feature selection that combines the Particle Swarm Optimization (PSO) and Differential Evolution
Feature Selection (DEFS) for minimizing the time complexity. A binary Support Vector Machine (SVM) classifier
categorizes the 13 normal and 75 abnormal images from 60 patients. Finally, the patients affected by DR are further classified
by Multi-Layer Perceptron (MLP). The experimental results exhibit better performance of accuracy, sensitivity, and
specificity than the existing methods.
Keywords: Diabetic Retinopathy (DR), Optical Coherence Tomography (OCT), Histogram of Oriented Gradient (HOG), Complete
Local Binary Pattern (CLBP), Particle Swarm Optimization (PSO), Differential Evolution Feature Selection (DEFS),
Multi-Layer Perceptron (MLP).
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