Advancements in Ocular Vascularization Analysis Using Deep Learning Techniques – A Systematic Review

F Rahman ^1*, Akanksha Mishra ¹, Sushree Sasmita Dash ¹

This review shows an advanced machine learning method for efficient segmentation and classification of blood vessels in medical images, revolutionizing diagnostic precision.

Abstract

In this review, we have investigated the critical examination of ocular vascularization for clinical assessment, specifically in the context of retinopathies associated with systemic disorders. The study emphasizes the impact of high blood pressure and diabetes on the development of hypertensive and diabetic retinopathy, respectively. Traditionally, identifying and classifying blood vessels in medical imaging has been time-consuming and prone to errors. The introduction of Machine Learning (ML), particularly Deep Learning (DL) methods, has revolutionized the process, allowing for more efficient and precise identification and categorization of blood vessels. The research proposes a cutting-edge ML framework utilizing Convolutional Neural Networks (CNNs) and ensemble learning to optimize blood vessel segmentation and classification. The primary goal is to enhance both speed and efficiency, addressing common challenges in processing medical images. The application of these advanced algorithms not only represents a technological achievement but also a significant stride towards personalized and accurate medical care. Our review consolidates existing research findings, compares methodologies, and highlights the progress in ML techniques that have improved the speed and efficacy of blood vessel segmentation, particularly in ophthalmology.

Keywords: Ocular vascularization, Retinopathies, Machine learning in medical imaging, Blood vessel segmentation, Retinal image analysis

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