Detection of Diabetic Retinopathy Using Discrete Wavelet-Based Center-Symmetric Local Binary Pattern and Statistical Features

• DOI: 10.1007/s10278-024-01243-2
• Publication Date: 2024-09-05T20:26:22Z
• Authors: Imtiyaz Ahmad; Vibhav Prakash Singh; Manoj Madhava Gore
• Keywords: Center (category theory)

Computer-aided diagnosis (CAD) system assists ophthalmologists in early diabetic retinopathy (DR) detection by automating the analysis of retinal images, enabling timely intervention and treatment. This paper introduces a novel CAD based on global multi-resolution images. As first step, we enhance quality images applying sequence preprocessing techniques, which include median filter, contrast limited adaptive histogram equalization (CLAHE), unsharp filter. These steps effectively eliminate noise Further, these are represented at multi-scales using discrete wavelet transform (DWT), center symmetric local binary pattern (CSLBP) features extracted from each scale. The CSLBP decomposed capture fine coarse details fundus Also, statistical to characteristics provide comprehensive representation performances evaluated benchmark dataset two machine learning models, i.e., SVM k-NN, found that performance proposed work is considerably more encouraging than other existing methods. Furthermore, results demonstrate when wavelet-based combined with features, they yield notably improved compared individually.