Rough set based feature selection model for diabetic retinopathy classification

Medical image processing is an essential challenge in a wide range of applications in today’s clinical scenario. Such applications can be served throughout the clinical course of events, not just in the diagnostic environment but also in the planning, execution, and progression of surgical and radiation procedures. The role of medical imaging information retrieval and processing is significant in surgical planning and tracking the progress of diseases. So, using state of the art computing techniques, researchers have made efforts to propose an effective automated technique to determine Diabetic Retinopathy (DR) based on significant medical image features and the patient’s clinical history. In this work, an intelligent graph-based methodology is proposed, considering the concepts from Rough Set Theory for feature selection. Based on several centrality metrics of graphs, a voting method is proposed to identify important features, resulting in better classification outcomes for diabetic retinopathy. Proposed methods are compared with several existing baseline feature selection approaches and provide better feature selection outcomes than those existing approaches. The result shows significantly better classification outcomes with respect to classical classification approaches.