Automated Detection of Glaucoma from Fundus Images Using Deep Convolutional Networks

This study uses Convolutional Neural Networks on retinal images to meet the critical requirement for precise and understandable automated glaucoma identification. Utilizing secondary data collected from credible ophthalmic databases, a deductive strategy was used within a descriptive study framework while adopting an interpretive mindset. Through meticulous training on a varied dataset that included both healthy patients and cases with various stages of glaucoma, the design of CNN was refined. With a precision of 92% as well as a specificity rating of 94%, its CNN-based myopia detection system demonstrated outstanding diagnostic accuracy. Additionally, the model proved to be robust across a range of demographic groupings, guaranteeing consistent results regardless of age, sexual orientation, or ethnicity. In particular, the approach demonstrated exceptional adaptability to differences in disease delivery, excelling especially the identification of the initial stages glaucoma. Features of interpretability were added to increase decision-making visibility. Insights about the algorithm's diagnostic procedure were gained through the use of saliency maps members' feature visualization approaches, which enabled doctors and the computer program to make decisions together. Although the system shows promising findings, more study is advised to evaluate its performance in actual clinical situations and to improve interpretability methods. Automated glaucoma detection needs to continue to progress, and this requires prospective data collection initiatives and adherence to strict ethical standards. This study sets the groundwork for a highly useful and understandable instrument that will transform the early detection and treatment of glaucoma