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Diabetic 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.

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