{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/enhanced-optic-disk-and-cup-segmentation-with","title":"Enhanced Optic Disk and Cup Segmentation with Glaucoma Screening from Fundus Images using Position encoded CNNs","arxiv_id":"1809.05216","date":"2018-09-14","proceeding":null,"authors":["Vismay Agrawal","Avinash Kori","Varghese Alex","Ganapathy Krishnamurthi"],"abstract":"In this manuscript, we present a robust method for glaucoma screening from\nfundus images using an ensemble of convolutional neural networks (CNNs). The\npipeline comprises of first segmenting the optic disk and optic cup from the\nfundus image, then extracting a patch centered around the optic disk and\nsubsequently feeding to the classification network to differentiate the image\nas diseased or healthy. In the segmentation network, apart from the image, we\nmake use of spatial co-ordinate (X \\& Y) space so as to learn the structure of\ninterest better. The classification network is composed of a DenseNet201 and a\nResNet18 which were pre-trained on a large cohort of natural images. On the\nREFUGE validation data (n=400), the segmentation network achieved a dice score\nof 0.88 and 0.64 for optic disc and optic cup respectively. For the tasking\ndifferentiating images affected with glaucoma from healthy images, the area\nunder the ROC curve was observed to be 0.85.","url_abs":"http://arxiv.org/abs/1809.05216v1","url_pdf":"http://arxiv.org/pdf/1809.05216v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"enhanced-optic-disk-and-cup-segmentation-with","repo_url":"https://github.com/koriavinash1/Optic-Disk-Cup-Segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":null,"task_name":"Position"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}