Glaucoma Detection Through Multi-Modal Integration of Retinal Images and Clinical Biomarkers
We developed a hybrid fusion model integrating Vision Transformers and ResNet50 with clinical biomarker analysis to enhance glaucoma detection accuracy up to 99.4%. This innovative approach combines deep learning with clinical data to provide a comprehensive, highly accurate diagnostic tool, significantly improving early detection and management of glaucoma.