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