A Hybrid PSO-DEFS Based Feature Selection for the Identification of Diabetic Retinopathy

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.