Retinal fundus image classification for diabetic retinopathy using SVM predictions

Diabetic Retinopathy (DR) is one of the leading causes of blindness in all age groups. Inadequate blood supply to the retina, retinal vascular exudation, and intraocular hemorrhage cause DR. Despite recent advances in the diagnosis and treatment of DR, this complication remains a challenging task for physicians and patients. Hence, a comprehensive and automated technique for DR screening is necessary, which will give early detection of this disease. The proposed work focuses on 16 class classification method using Support Vector Machine (SVM) that predict abnormalities individually or in combination based on the selected class. Our proposed work comprises Gaussian mixture model (GMM), K-means, Maximum a Posteriori (MAP) algorithm, Principal Component Analysis (PCA), Grey level co-occurrence matrix (GLCM), and SVM for disease diagnosis using DR. The proposed method provides an accuracy of 77.3% on DIARETDB1 dataset. We expect this low computational cost will be helpful in the medicine and diagnosis of DR.