An Improved Robust Fuzzy Local Information K-Means Clustering Algorithm for Diabetic Retinopathy Detection

According to the International Diabetes Federation (IDF), roughly 33% of individuals affected by diabetes exhibit diagnoses encompassing diverse severity of diabetic retinopathy. In the year 2020, approximately 463 million adults within the age bracket of 20 to 79 were documented as diabetes sufferers on a global scale. Projections suggest a rise to 700 million by 2045. The proposed automated diabetic retinopathy detection methods aim to reduce the workload of ophthalmologists. The study presents the Robust Fuzzy Local Information K-Means Clustering algorithm, an advanced iteration of the classical K-means clustering approach, integrating localized information parameters tailored to individual clusters. Comparative analysis is conducted between the performance of Robust Fuzzy Local Information K-Means Clustering and Modified Fuzzy C Means clustering, which incorporates a median adjustment parameter to augment Fuzzy C Means for diabetic retinopathy detection. The results are evaluated on three datasets: IDRiD, Kaggle, and fundus images collected from Shiva Netralaya Center, India. Achieving a 94.4% accuracy rate and an average execution time of 17.11 seconds, the proposed algorithm aims to categorize a substantial volume of retinal images, thereby improving performance and meeting the crucial demand for prompt and precise diagnoses in diabetic retinopathy healthcare.