Diabetic Retinopathy is a retinal eye disease that affects people with Diabetes Mellitus. DM is metabolic disorder and it is caused by the high glucose level in blood. This leads to eye deficiency called Diabetic Retinopathy (DR). In this paper, we proposed iGWO algorithm for the detection and diagnosis of DR stages and Region growing technique is used for segmentation of the images. Various preprocessing techniques are followed for the better enhancement of the images. An improved Grey Wolf Optimization (iGWO) algorithm are proposed to obtain the global optimum. Convnets are utilized for DR categorization. The iGWO-FFOCNN model is compared with existing technologies like SVM-GSO (Support Vector Machines – Glowworm Swam Optimization), PSO-CNN (Particle Swarm Optimization-Convolutional Neural Networks), CNN (Convolutional Neural Networks), DCNN-EMFO (Deep Convolutional Neural Networks- Enhanced Moth Flame Optimization) and MACO-CNN (Modified Ant Colony Optimization–CNN). The APTOS DR Dataset utilized for the proposed metodology. The effort is made for identifying the DR stages by using iGWO. Finally, the results confirm that iGWO-FFOCNN technique yields good performance than compared to the existing techniques in relation to F-measure, precision, recall and also in DSC (Dice Similarity Coefficient), JSC (Jaccard Similarity Coefficient) and time period.