Comparative Analysis of FCN and U-Net for Retinal Blood Vessels Segmentation: A Performance Evaluation

Abstract

Accurate retinal blood vessels segmentation is essential for early detection and diagnosis of several diseases. This study presents a comparative analysis between U-Net and FCN for retinal blood vessel segmentation. The aim is to assess their performance across four key metrics: sensitivity, accuracy, precision, and specificity, alongside training time. The analysis revealed differences in performance between the two models in terms of segmentation quality and efficiency. U-Net consistently outperforms FCN in terms of accuracy and precision, showcasing its detailed understanding of vessel characteristics and superior segmentation performance. However, FCN demonstrates competitive accuracy and specificity, and shorter training time, suggesting its utility in situations prioritizing computational efficiency.

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