Browsing by Author "Raja, Nisan Pranavah"

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    VasFAZ-Net: A Hybrid Loss-Optimized E cientNet-scSE Network for Retinal Vasculature and FAZ Segmentation in OCTA
    (Institute of Mathematics and Computer Science, 2026-05-01) Raja, Nisan Pranavah; Gopi, Varun P.; Francis, Bibin; Devi, Chelli N.
    Accurate 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.