Advanced Deep Learning Approaches for Early Detection of Diabetic Retinopathy in Retinal Images

Diabetic Retinopathy (DR) is one of the major causes of blindness worldwide and a complication of diabetes. Therefore, early identification and intervention are important to reduce the extent of vision loss and improve prognosis for patients. This paper presents an improved model for screening DR at its initial stage in retinal images using deep learning methods. The developed system employs convolutional neural networks (CNNs) to detect abnormalities in retinal images that indicate early signs of DR, thereby increasing the chances of screening in clinics. The proposed system utilizes large-scale Kaggle DR and Messidor datasets, with an optimized CNN for the analysis of retinal images. To address issues related to variations in image quality and acquisition methods, various data enhancement strategies are incorporated into the model. Additionally, an interpretability module based on Grad-CAM is included to explain the model's classifications and build trust with clinicians. Based on experimental results, it can be concluded that the proposed model demonstrates higher accuracy, sensitivity, and specificity compared to benchmark methods. This system shows potential in reducing the workload of ophthalmologists and improving early DR screening, particularly in resource-limited healthcare settings. Furthermore, it can be extended to other ophthalmic diseases, offering significant benefits to the broader healthcare system.