Novel Methods for Diagnosing Glaucoma: Segmenting Optic Discs and Cups using Ensemble Learning Algorithms and CDR Ratio Analysis.

Glaucoma, a multifactorial group of eye diseases characterized by progressive damage to the Glaucoma, a group of progressive optic neuropathies, is a leading cause of irreversible blindness globally, affecting millions of individuals. This research addresses the critical task of glaucoma identification through the segmentation of the optic disc and optic cup using ensemble learning algorithms, Unet and Gnet. The study leverages the capabilities of these algorithms to enhance the accuracy of the segmentation process, a crucial step in early glaucoma detection. A meticulously curated dataset of ophthalmic images is utilized, with a focus on preprocessing techniques includes resizing, normalization, filtering and contrast enhancement process to optimize the input quality. The proposed architectures of Unet and Gnet, highlighting their suitability for segmenting the optic disc and cup. The experimental setup involves rigorous training, with an emphasis on fine-tuning the models for segmentation tasks. Evaluation metrics, including Dice coefficient and sensitivity, are employed to assess the precision of the segmentation results. The outcomes demonstrate the efficacy of ensemble Unet and Gnet. Consistently achieving accuracy levels surpassing 98.90% across various datasets, the suggested model demonstrates exceptional performance in accurately categorizing severe cases. The study concludes with insights into the potential clinical impact of improved optic disc and cup segmentation on early glaucoma diagnosis, emphasizing the significance of ensemble learning in advancing ophthalmic image analysis for medical applications. [ABSTRACT FROM AUTHOR]