This study proposes two deep learning models, DiploNet-1-Res and Diplo-Net-2-Res, and it’s Inspired by Diplodocus, for optic disc and optic cup segmentation in glaucoma and compares their performance with state-of-the-art models. The evaluation is performed on four publicly available retinal image datasets—ACRIMA, Drishti_GS, ORIGA, and REFUGE—with mosaic augmentation applies to the training sets. Each dataset is split into subsets of 70%, 15%, 15% for training, validation, and testing respectively. The experimental results show that DiploNet-1-Res achieved optic-disc mDice of 0.950–0.972 and mIoU of 0.906–0.946, with optic-cup mDice of 0.855–0.937 and mIoU of 0.750–0.884; DiploNet-2-Res achieved optic-disc mDice of 0.955–0.975 and mIoU of 0.915–0.952, with optic-cup mDice of 0.877–0.943 and mIoU of 0.785–0.894. Regarding to this, the results demonstrate that DiploNet-2-Res achieves superior segmentation accuracy, consistently outperforming other models in terms of mean Dice and mean intersection over union across all datasets. These results demonstrate that multi-scale feature extraction, deep semantic encoding, and enhanced spatial retention markedly improve segmentation performance and emphasize deep learning promise for scalable glaucoma screening and automated diagnosis in clinical decision-making.