Enhancing Diabetic Retinopathy Diagnosis with ResNet-50-Based Transfer Learning: A Promising Approach

Abstract

Diabetic retinopathy is considered the leading cause of blindness in the population. High blood sugar levels can damage the tiny blood vessels in the retina at any time, leading to retinal detachment and sometimes glaucoma blindness. Treatment involves maintaining the current visual quality of the patient, as the disease is irreversible. Early diagnosis and timely treatment are crucial to minimizing the risk of vision loss. However, existing DR recognition strategies face numerous challenges, such as limited training datasets, high training loss, high-dimensional features, and high misclassification rates, which can significantly affect classification accuracies. In this paper, we propose a ResNet-50-based transfer learning method for classifying DR, which leverages the knowledge and expertise gained from training on a large dataset such as ImageNet. Our method involves preprocessing and segmenting the input images, which are then fed into ResNet-50 for extracting optimal features. We freeze a few layers of the pre-trained ResNet-50 and add Global Average Pooling to generate feature maps. The reduced feature maps are then classified to categorize the type of diabetic retinopathy. We evaluated the proposed method on 40 Real-time fundus images gathered from ICF Hospital together with the APTOS-2019 dataset and used various metrics to evaluate its performance. The experimentation results revealed that the proposed method achieved an accuracy of 99.82%, a sensitivity of 99%, a specificity of 96%, and an AUC score of 0.99 compared to existing DR recognition techniques. Overall, our ResNet-50-based transfer learning method presents a promising approach for DR classification and addresses the existing challenges of DR recognition strategies. It has the potential to aid in early DR diagnosis, leading to timely treatment and improved visual outcomes for patients.