Published Paper Details:

Retinal vascular segmentation plays a crucial role in the early diagnosis and prognosis of eye diseases such as diabetic retinopathy and age-related macular degeneration. Machine learning models have emerged as powerful tools for automating this segmentation process, enabling accurate and efficient analysis of retinal images. This survey explores innovative approaches in retinal vascular segmentation with a focus on machine learning techniques.We review a variety of machine learning models applied to retinal vascular segmentation, including deep learning architectures such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and their combinations. The survey highlights key methodologies, challenges, and advancements in the field, aiming to provide a comprehensive understanding of the current state-of-the-art. Throughout the review, we identify critical gaps and limitations in existing literature, including issues related to model generalization across diverse datasets, robustness to image artifacts, and scalability for large-scale clinical applications. We discuss promising directions for future research, emphasizing the importance of interpretable and clinically validated segmentation models. By synthesizing insights from various machine learning approaches, this survey contributes to the ongoing efforts in developing accurate and reliable tools for eye disease forecasting based on retinal imaging data. The findings presented here serve as a valuable resource for researchers and practitioners working at the intersection of machine learning and ophthalmic healthcare, ultimately aiming to improve patient outcomes through early disease detection and personalized treatment strategies.