Abstract
Diabetic retinopathy (DR) can cause blindness and vision impairment. This degenerative eye condition may lead to an irreversible vision loss. The prevalence of vision impairment and blindness caused by DR emphasizes the critical need for better screening and therapy measures. DR aetiology involves persistent hyperglycemia-induced microvascular abnormalities, oxidative stress, inflammatory reactions, and retinal blood flow changes. Common screening methods for retinal issues include fundus photography, OCT, and fluorescein angiography. For those with diabetic macular edema (DME), it is a common cause of vision loss. Our goal is to develop an automated, cost-effective method for identifying diabetic retinal disease specimens. This study introduces a faster R-CNN method for detecting and classifying DR lesions in retinal images. Those are classified across five different classes. An extensive analysis of 88,704 images from a Kaggle dataset indicates the efficiency of the proposed model, with a reasonable accuracy of 98.38%. The proposed method is robust in disease localization and classification tasks and it has outperformed other existing studies in DR recognition. On evaluating cross-datasets in Kaggle and APTOS, the model has yield better results during training and testing phases.