Towards Efficient Diabetic Retinopathy Diagnosis: A Comparative Study of Classification Techniques

Diabetic retinopathy (DR), a leading cause of vision loss among individuals with diabetes, necessitates accurate and timely diagnosis for effective management. This paper evaluates two classification models: the gray-level co-occurrence matrix (GLCM) and the convolutional neural network (CNN) ResNet-50 architecture, for automated DR diagnosis. The study employs retinal images from Kaggle and Zenodo datasets, assesses model performance, and optimizes the ResNet-50 parameters to enhance classification accuracy. The results demonstrate the superior performance of ResNet-50 compared to GLCM. The achieved accuracies for distinguishing normal and diabetic retinal images are $\mathbf{9 7. 8 8 9 \%}$ and $\mathbf{9 2. 0 5 3} \%$, based on Kaggle and Zenodo datasets, respectively. This indicates a robust performance of ResNet- 50 in multi-class classification tasks and highlights its potential for improving DR diagnosis systems. These findings underscore the significance of advanced computational techniques in early DR detection, offering enhanced diagnostic efficiency and potentially alleviating healthcare burdens.