Integrated Deep Learning Framework for glaucoma detection, Optic Disk/Cup Segmentation, and CDR Calculation

The proposed system is a two stage deep learning framework for automated glaucoma detection and analysis of fundus images in this project. Once the image is processed, the system hands it off to a ResNet model to predict whether the image is positive for glaucoma or not. When it is a positive case, it segments the optic disc and cup using a YOLOv5 model and can then attempts to precisely calculate the cup to disc ratio (CDR), a key parameter in glaucoma risk determination. With combined classification and segmentation integration the proposed streamlines workflow greatly reducing manual examinationdependency and creates a more objective and accurate diagnostic support for ophthalmology. Clinicians can upload images, view segmentation results, and access CDR values in a user friendly system to help make appropriate clinical decisions. The proposed system is an efficient and accurate framework with the potential for implementation in real time for early glaucoma diagnosis in high demand or resource limited locations or situations or settings.