A Study of Ocular Disease Cataract Recognition Using Deep Learning Approach

Abstract

Deep learning models have exhibited great potential in medical imaging, especially in the automated identification of eye disorders utilizing fundus pictures. This project intends of eye illness diagnosis.This research focuses on four important CNN architectures: VGG19, VGG16, InceptionV3, ResNet50, and DenseNet121, comparing their performance in terms of accuracy, precision, and recall. The experimental findings suggest that the VGG19 model attained the maximum accuracy of 96.33%, with precision and recall values of 0.96 and 0.97, respectively. The VGG19 model's higher performance may be due to its deep architecture, which efficiently catches complicated patterns in the fundus pictures. Other models, such as VGG16, ResNet50, and DenseNet121, also displayed strong performance, with accuracies of 95.41%, 94.95%, and 94.95%, respectively. However, the InceptionV3 model trailed, with an accuracy of 84.86%, showing the limitations of its complicated design in this application. The fundamental aim of this effort is to promote the early identification and diagnosis of eye illnesses by automated, accurate, and efficient deep learning approaches. By assessing several CNN designs, this research determines the most successful model for clinical deployment, hence possibly decreasing the strain on healthcare providers and increasing patient outcomes. The research presented here not only adds to the expanding body of knowledge in medical imaging but also illustrates the potential of AI in changing healthcare diagnostics, underlining the necessity for continual innovation and ethical concerns in the deployment of new technologies.