A New Diabetic Retinopathy Classification Approach Based on Normalizer Free Network

Diabetic retinopathy (DR) is a complication resulting from diabetes mellitus, caused by damage to the blood vessels in the retina due to excess glucose in the blood. This condition is one of the leading causes of vision loss in adults with diabetes. Early detection and appropriate treatment are crucial to prevent the progression of the disease. The main objective of this study is to develop an advanced tool for classifying retinal image photographs. To achieve this, we used the Normalizer Free Neural Network (NFNet) as a feature extractor, combining it with machine learning techniques through the method of transfer learning. We evaluated the effectiveness of our model by comparing its results with those of various established and recognized convolutional neural network architectures in the literature. The results show that, using the NFNet, we achieved an accuracy of 99.83% and an F1-Score of 99.46% when combined with support vector machines using radial basis function kernel. Given the significance of these results, the next step is to explore the possibility of developing a diagnostic support tool using the developed methodology.