A Design of an Effective Methodology to Detect Glaucoma Disease using Intelligent Learning Strategy

Globally, glaucoma is now among the leading causes of blindness. It is a chronic, irreversible illness. Screening for glaucoma and detecting it early are crucial in the fight against this illness. Here, deep learning shows great promise. Two deep learning architectures, the Convolutional Neural Network (CNN) model and the Improved Learning Strategy to Predict Glaucoma (ILSPG) model, have been evaluated in this article. In order to diagnose glaucoma, the DCNN method is utilized. Ultimately, this area of study aims to help doctors diagnose glaucoma by developing methods to spot the disease in retinal fundus pictures. Because glaucoma can have both beneficial and bad effects on the model, the result of the model might be good or bad. Several measures are utilized in the process of evaluating models, including accuracy, precision, specificity, and reliability. In order to determine the accuracy of the proposed model, an additional comparison study is conducted. The results are contrasted with a deep learning-based classification system that uses convolutional neural networks. The results demonstrate, without a doubt, that the proposed system is effective.