Optimization and Application of a Deep Learning-Based Multi-Class Retinal Vessel Segmentation Model

With the development of deep learning, its applications in computer vision and medical image processing have become increasingly widespread, particularly excelling in tasks like image segmentation. Convolutional neural networks (CNNs), known for their ability to perform multi-level feature extraction and their adaptable structural design, have become pivotal in the field of medical image analysis. Retinal vessel multi-class segmentation, which is crucial for the early diagnosis of ophthalmic diseases and the extraction of pathological features, has increasingly become a central focus of research. This paper addresses the field of multi-class segmentation of retinal vessels and proposes an optimized deep learning-based model architecture, significantly improving segmentation accuracy and robustness. The model innovatively integrates the feature extraction capabilities of CNN sand incorporates techniques such as the Adam optimizer, dynamic learning rate adjustment, regularization constraints, and early stopping to enhance convergence efficiency and generalization performance. Compared to traditional single-class segmentation methods, this model effectively distinguishes arteries, veins, and their overlapping regions, demonstrating strong adaptability in complex vessel networks and ambiguous areas. Through the application of data augmentation methods, including rotation, flipping, and scaling, the preprocessing process boosts sample diversity, which in turn enhances the model's robustness against varied data. Experimental results reveal that the optimized model delivers outstanding performance across various segmentation tasks and achieves critical breakthroughs in multi-class vessel recognition. While there remains room for improvement in handling intricate details and complex scenarios, the model's multi-class segmentation capabilities and medical application value offer significant insights for future research.