Link to the Paper: https://ieeexplore.ieee.org/document/9031947

Diabetic retinopathy disease is constantly on the rise across the globe which causes blurry vision, partial and total blindness to diabetic affected people. Advancement of Biomedical imaging with signal processing and machine learning algorithms make ease of the prediction of Diabetic retinopathy to a greater extent. In this study, the retinal image is taken from a fundus camera of both healthy and diabetic retina. Image pre-processing techniques, morphological operations are used to detect the statistical features and the histogram-based feature is extracted by using Discrete Wavelet Transform (DWT) which is the novel contribution of the proposed algorithm. These features are classified by any machine learning approach (K-Nearest Neighbors, Support Vector Machine and Artificial Neural Network) to predict DR accurately and efficiently following a cross-validation approach.

The experimental result shows that the obtained accuracy and other measures are better for DWT with histogram features, while the statistical features have a very lower value. While SVM and ANN performed better in case of statistical features but the ANN performance is better as compared to the KNN and SVM for DWT features. However, as a whole, the DWT based histogram-based features are suggested for the diabetic retinopathy classification of fundus images.