Advancements in Glaucoma Diagnosis: Composite Dilated U-Net Based Convolution for Image Segmentation

Accurate segmentation of the optic disc and cup in fundus images is critical for the early diagnosis and management of glaucoma, a leading cause of irreversible blindness. This research explores the application of advanced deep learning techniques for this task, with a focus on the development and evaluation of the Composite Dilated U-Net Based Convolution (CDUBC) framework. CDUBC integrates the strengths of various state-of-the-art models, including U-Net's encoder-decoder structure, multi-scale convolutional neural networks (CNNs), attention mechanisms, and dilated convolutions. By combining these approaches, CDUBC aims to enhance segmentation accuracy by capturing multi-scale contextual information while preserving critical details. This comprehensive review of related works underscores the transformative impact of deep learning in medical image analysis, highlighting significant advancements and ongoing challenges. The proposed CDUBC framework demonstrates promising improvements in the precision of optic disc and cup segmentation, paving the way for more effective glaucoma diagnosis and management. Future research should focus on integrating these advanced segmentation techniques into clinical workflows, validating their performance across diverse populations, and exploring their potential for real-time clinical applications.