Classification of retinal fundus image using MS-DRLBP features and CNN-RBF classifier

The most common retinal diseases that are to be diagnosed are Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD) and Choroidal Neovascularization (CNV). For the people above 60 years of age, detection of these retinal diseases is an important task for treatment that reduces the risk of vision loss. Retinal fundus images play a significant role in the detection of DR, AMD and CNV disease diagnosis and treatment. The existing techniques for the detection of DR, AMD and CNV have not fulfilled with the classification accuracy of the retinal diseases effectively. This research work proposes an efficient classification framework for retinal fundus image recognition to overcome these drawbacks. Initially, the input image from the publicly available STARE database is preprocessed with the following three steps (a) Specular reflection removal and smoothing, (b) contrast enhancement and (c) retinal region expansion. With the preprocessed image, the features are extracted using Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP), based on RGB component selection, Gradient operation, and LBP descriptor. Finally, classification was done using hybrid Convolution Neural Network (CNN) and Radial Basis Function (RBF) model (CNN-RBF) which classifies the retinal fundus images into four classes such as DR, AMD, CNV and Normal (NR). Experimental results of the proposed method gives an accuracy of 97.22% compared with the existing other methodologies.

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