Hybrid Deep Learning Framework for Enhanced Automated Fundus Image Segmentation in Glaucoma Screening

Glaucoma, a major cause of irreversible blindness, requires accurate and early detection of optic disc (OD) and optic cup (OC) deformations in fundus images to prevent optic nerve damage. A Hybrid Deep Learning Framework for automatic OD and OC segmentation is presented in this work. To efficiently capture pertinent information for glaucoma detection, the model incorporates advanced features like squeeze-and-excitation (SE) blocks, multi-scale attention processes, and Atrous Spatial Pyramid Pooling (ASPP) modules. Experimentation was conducted using the REFUGE dataset for training, and the model was further evaluated on six publicly available fundus image datasets: ORIGA, DRISHTI-GS1, HRF, Dr. HAGIS, BEH, and DRIVE, constituting a cross-dataset assessment to evaluate generalization capability across diverse imaging conditions. The segmentation performance of the proposed model is remarkable, achieving high Formula: see text1-scores for both OD and OC across multiple datasets. The framework outperformed several state-of-the-art designs, including modified U-Net and linear-dual attention mechanisms, with improvements of up to 6.56% in OC and 2.59% in OD segmentation. The model demonstrated excellent OD segmentation on the DRIVE dataset, achieving an Formula: see text1-score of 0.9848, highlighting its robustness, accuracy, and versatility across diverse fundus imaging conditions.