Fusing dual‐tree quaternion wavelet transform and local mesh based features for grading of diabetic retinopathy using extreme learning machine classifier

Abstract

AbstractDiabetic retinopathy (DR) is one of the most frequent microvascular complications of diabetes mellitus, which damages micro‐ and macrovascular systems. Hence, early detection and grading are important for its effective treatment. This study presents a comprehensive micro‐macro feature extraction algorithm for the grading of DR using retinal images. The method employed is a mutliresolutional microtechnique, based on dual‐tree quaternion wavelet transform fused with local mesh patterns. Since the pixel level model is unable to capture macrolevel features and is difficult for efficient decision‐making, this process additionally proposes a macrolevel feature extraction technique based on feature gradients. The macrolevel descriptor considers a group of pixels to find feature gradients of macrolevel lesions. Feature extracted using the micro‐macro approaches is summarized, and a comparison study using three machine learning classifiers is considered. Performance of the classifiers is determined by conducting a 10‐fold cross‐validation procedure. Among the classifiers, the highest classification accuracy of 93.2% is exhibited by radial basis function kernel extreme learning machine. Simulation results illustrate the adaptability and competency of the novel micro‐macro approach with high accuracy and sensitivity. The promising result assures the excellence of the proposed method for automated DR grading over other state‐of‐the‐art techniques.