ABSTRACT Precise segmentation of critical retinal structures, including the optic cup, optic disc, and vascular bifurcation sites, is essential for the early identification and management of severe ocular conditions such as glaucoma, diabetic retinopathy, hypertensive retinopathy, and age‐related macular degeneration. This study proposes a hybrid multi‐attention residual‐based UNet (MRUNet+), a deep learning (DL) model developed to address the complex nature of retinal image processing using an advanced attention mechanism to enhance segmentation precision. MRUNet+ was trained on many high‐quality datasets, including DRIVE, CHASEDB1, Drishti‐GS1, RIM‐ONE and REFUGE1, enabling it to address prevalent imaging issues, such as variations in contrast, resolution and the presence of abnormalities. The model has exhibited markedly enhanced performance compared to recent methodologies, with improved accuracy metrics, Dice coefficients and intersection over union (IoU), particularly in low‐contrast or complex anatomical regions. Due to its adaptability and precision, MRUNet+ has the potential to function as a pivotal instrument in clinical settings, providing automated retinal assessments that aid in the prompt detection of various retinal and vascular pathologies, thereby improving patient management.
Khan et al. (Thu,) studied this question.