Deep CNN Architecture for Automated Identification and Severity Grading Of Diabetic Retinopathy

Diabetic Retinopathy (DR) is among the most prevalent microvascular diabetic complications and amajor cause of avoidable blindness in the world. Early identification and correct grading of DR are critical forthe initiation of prompt treatment and prevention of vision loss. Nonetheless, expert ophthalmologist-dependent retinal fundus image manual evaluation is time-consuming, prone to subjectivity, and highlyreliant on skilled ophthalmologists. In order to deal with these issues, in this research, an automated, deeplearning approach and Convolutional Neural Networks (CNN) is suggested for detecting the severity ofDiabetic Retinopathy using retinal fundus images. The CNN architecture classifies these retinal scan imagesinto five clinically accepted stages: NO DR, Mild Stage DR, Moderate Stage DR, Severe Stage DR, andProliferative Stage DR. The architecture includes several convolutional layers followed by BatchNormalizationh,aRrdeLU Aectxiuvadtaiotens,, and Msoaftx Pooleinxugdoapteesr,ationsantod extracthierarchical features of retinal abnormalities. nAeFouvlalysccuolanrnizeacttieodn.layer approach is used toavoid overfitting and improve generalization. The last softmax layer gives probabilistic classification output.The model is learned and being tested on a vast annotated dataset of retinal images with high classificationaccuracy and sensitivity for all DR grades. Experimental results have proven that the suggested CNN modelefficiently detects the DR stages with great reliability. The system provides a scalable and non-invasivemethod to aid ophthalmologists in early screening and diagnosis, thus facilitating better treatment,particularly for resource-poor areas