Abstract: The extent, width, curvature, and branching pattern of the retina’s blood vessels, among other structural character- istics, plays a major role in the assessment of diseases related to diabetes and heart, hypertension. Through our research, we offer a procedure for segmenting the retinal vascular system firm FCNs. From every retinal image, thousands of patches are gathered, and these patches are rotated before being sent via the network for Data Augmentation. For vessel segmentation, two FCNs are used: LadderNet Architecture and U-Net Architecture. Three well-known datasets: STARE, CHASE_DB1, and DRIVE are used to evaluate our methodology. When compared to the other previously mentioned methodologies, our strategy indicates better performance.
Keywords: Retina, Vessels, Convolutional, U-Net, Ladder-Net, Opthalmology.
Cite:
Bhupathi Rayudu Inaganti, Prasanth Yenumula, Selvam K, Jahnavi Bandaru,Varaha Varshini Naidu Polamarasetty, "Retinal Vessel Segmentation Using CNN And U-Net Architecture", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13356.
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DOI:
10.17148/IJARCCE.2024.13356
