LBP Feature Extraction for Early Detection of Diabetic Eye Diseases

Abstract

Abstract: Diabetic Eye Diseases, such as diabetic retinopathy, pose a significant threat to the vision of individuals with diabetes. Early detection of these diseases is crucial for effective treatment and prevention of severe vision loss. In this study, we propose a novel approach for the early detection of Diabetic Eye Diseases using Local Binary Patterns (LBP) feature extraction. The LBP method captures local texture patterns in retinal images, allowing for the identification of characteristic features associated with the diseases. To evaluate the effectiveness of our approach, we utilized a dataset consisting of retinal images from diabetic patients with varying stages of the diseases. The dataset was pre-processed using standard techniques, and LBP features were extracted from the segmented retinal regions. A classification algorithm such as SVM, Random Forest, Adaboost was employed to differentiate between normal and diseased retinal images based on the extracted LBP features. Our experimental results demonstrated the efficacy of the proposed LBP feature extraction method, achieving a high accuracy of 79% using Adaboost in MESSIDOR and 85% using SVM in DIARETDB0 dataset in early detection of Diabetic Eye Diseases