Raja, Nisan PranavahGopi, Varun P.Francis, BibinDevi, Chelli N.2026-11-022026-11-022026-05-0115614042https://doi.org/10.56415/csjm.v34.01http://nitt.ndl.gov.in/handle/123456789/389Accurate segmentation of ne retinal vessels and the Foveal Avascular Zone (FAZ) in OCTA images remains challenging due to low contrast, noise, and class imbalance. To address this, a lightweight deep learning framework called VasFAZ-Net is proposed, which uses an E cientNet-B0 encoder, a parallel spatial and channel squeeze-and-excitation (scSE) decoder, and a hybrid Dice-Focal loss function, trained on the OCTA-500 dataset with both 3 mm and 6 mm scans. The proposed model achieved Dice scores of up to 88.48% for vessel segmentation and 97.98% for FAZ segmentation with only 8.8 million parameters and an inference time of 18.2 ms per image. These results demonstrate that the proposed method provides accurate, computationally e cient, and clinically deployable multi-target OCTA segmentation for retinal disease analysis.enArticle