koriavinash1/Optic-Disk-Cup-Segmentation
📦 Open Source Projectkoriavinash1
Deep learning-based segmentation for optic disc and cup to assist in glaucoma detection from fundus images.
The Optic-Disk-Cup-Segmentation repository offers a specialized deep learning solution tailored for retinal image analysis. At its core, the project implements a 57-layered deep convolutional neural network architecture designed to perform pixel-level segmentation of the optic disc and optic cup. This task is fundamental in ophthalmology, as the Cup-to-Disc Ratio (CDR) is a primary clinical indicator for glaucoma progression.
The repository includes the necessary logic to process fundus images, allowing the model to identify and delineate these specific regions despite the inherent complexities of retinal photography, such as varying illumination and anatomical diversity. By automating the segmentation process, the project reduces the subjectivity and time associated with manual annotation. It serves as a foundational implementation for developers and researchers looking to build or refine automated screening tools for ocular diseases using deep learning techniques.
💡Highlights
- ├─57-layered DCNN architecture
- ├─Automated CDR prediction
- └─Optimized for fundus imagery
🎯For
- ├─Medical AI Researchers
- ├─Ophthalmologists
- └─Computer Vision Engineers