Data Augmentation-Based Diabetic Retinopathy Classification and Grading with the Dynamic Weighted Optimization Approach

Abstract

Diabetic retinopathy is a vision-threatening complication of diabetes that affects the retina, the light-sensitive tissue at the back of the eye. This condition arises as a result of prolonged high blood sugar levels, which can damage the small blood vessels in the retina. Diabetic retinopathy typically progresses through different stages, starting with mild non-proliferative retinopathy, where small blood vessels in the retina become weakened and leak. The classification of diabetic retinopathy plays a fundamental role in assessing the effectiveness of treatment and monitoring the progression of the disease over time, ultimately contributing to the preservation of patients' vision and their overall quality of life. This research paper presents efficient technique for diabetic retinopathy (DR) classification and grading using data augmentation and a dynamic weighted optimization approach. The study contributes to the field of DR in several significant ways. Firstly, advanced data augmentation techniques are employed to generate diverse and representative features from retinal fundus images, enhancing the robustness and generalization capabilities of the models. Secondly, novel segmentation approaches, including multi-level Otsu thresholding and morphological operations, accurately localize and isolate affected regions in retinal images. Thirdly, innovative feature extraction and selection methods, such as Gray-Level Co-occurrence Matrix (GLCM) and dynamic Flemingo optimization, improve the selection of discriminative features for DR classification. Additionally, a novel cascaded voting ensemble deep neural network model is introduced, which combines the predictions of multiple learning algorithms to enhance classification performance. Lastly, the research addresses the grading of diabetic retinopathy by aligning the classification results with a standardized grading system, providing clinicians with accurate severity assessments for effective treatment decisions. Overall, this papers offers valuable insights and methodologies for improving the classification and grading of diabetic retinopathy, thereby contributing to the advancement of diagnosis and management strategies in the field.