{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T23:03:29Z","timestamp":1793747009500,"version":"4.6.1"},"reference-count":75,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T00:00:00Z","timestamp":1745798400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004070","name":"Khalifa University of Science, Technology and Research","doi-asserted-by":"publisher","award":["RC1-2018-KUCARS"],"award-info":[{"award-number":["RC1-2018-KUCARS"]}],"id":[{"id":"10.13039\/501100004070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004070","name":"Khalifa University of Science, Technology and Research","doi-asserted-by":"publisher","award":["8436010"],"award-info":[{"award-number":["8436010"]}],"id":[{"id":"10.13039\/501100004070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012824","name":"Mubadala Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012824","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1016\/j.compmedimag.2025.102559","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T19:06:31Z","timestamp":1745867191000},"page":"102559","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":25,"special_numbering":"C","title":["Enhanced glaucoma classification through advanced segmentation by integrating cup-to-disc ratio and neuro-retinal rim features"],"prefix":"10.1016","volume":"123","author":[{"given":"Rabia","family":"Pannu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Zubair","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7679-081X","authenticated-orcid":false,"given":"Muhammad","family":"Owais","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoaib","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Umair","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Syed Muhammad","family":"Usman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mousa Ahmed","family":"Albashrawi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irfan","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2025.102559_b1","series-title":"2014 International Conference on Robotics and Emerging Allied Technologies in Engineering","first-page":"321","article-title":"Detection of glaucoma using retinal fundus images","author":"Ahmad","year":"2014"},{"issue":"10","key":"10.1016\/j.compmedimag.2025.102559_b2","doi-asserted-by":"crossref","first-page":"2702","DOI":"10.4103\/ijo.IJO_92_21","article-title":"Identification of glaucoma from fundus images using deep learning techniques","volume":"69","author":"Ajitha","year":"2021","journal-title":"Indian J. Ophthalmol."},{"issue":"4","key":"10.1016\/j.compmedimag.2025.102559_b3","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3390\/sym10040087","article-title":"Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis","volume":"10","author":"Al-Bander","year":"2018","journal-title":"Symmetry"},{"key":"10.1016\/j.compmedimag.2025.102559_b4","doi-asserted-by":"crossref","first-page":"23894","DOI":"10.1109\/ACCESS.2021.3056641","article-title":"A comparative study of deep learning models for diagnosing glaucoma from fundus images","volume":"9","author":"Alghamdi","year":"2021","journal-title":"IEEE Access"},{"issue":"02","key":"10.1016\/j.compmedimag.2025.102559_b5","doi-asserted-by":"crossref","DOI":"10.1142\/S0218213023400018","article-title":"Glaucoma detection in retinal fundus images based on deep transfer learning and fuzzy aggregation operators","volume":"32","author":"Ali","year":"2023","journal-title":"Int. J. Artif. Intell. Tools"},{"issue":"3","key":"10.1016\/j.compmedimag.2025.102559_b6","doi-asserted-by":"crossref","first-page":"161","DOI":"10.5566\/ias.2346","article-title":"Rim-one dl: A unified retinal image database for assessing glaucoma using deep learning","volume":"39","author":"Batista","year":"2020","journal-title":"Image Anal. Ster."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102559_b7","article-title":"An artificial-intelligence-and telemedicine-based screening tool to identify glaucoma suspects from color fundus imaging","volume":"2021","author":"Bhuiyan","year":"2021","journal-title":"J. Ophthalmol."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102559_b8","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.survophthal.2022.08.005","article-title":"Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review","volume":"68","author":"Coan","year":"2023","journal-title":"Surv. Ophthalmol."},{"key":"10.1016\/j.compmedimag.2025.102559_b9","series-title":"2016 International Conference on Accessibility to Digital World","first-page":"181","article-title":"Detection of glaucoma using Neuroretinal Rim information","author":"Das","year":"2016"},{"key":"10.1016\/j.compmedimag.2025.102559_b10","series-title":"2024 IEEE 9th International Conference for Convergence in Technology","first-page":"1","article-title":"A CNN-based framework with focal loss for glaucoma detection on retinal fundus images","author":"Devika","year":"2024"},{"issue":"9","key":"10.1016\/j.compmedimag.2025.102559_b11","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1109\/TMI.2019.2903434","article-title":"Retinal image synthesis and semi-supervised learning for glaucoma assessment","volume":"38","author":"Diaz-Pinto","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2025.102559_b12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12938-019-0649-y","article-title":"CNNs for automatic glaucoma assessment using fundus images: an extensive validation","volume":"18","author":"Diaz-Pinto","year":"2019","journal-title":"Biomed. Eng. Online"},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102559_b13","first-page":"1057","article-title":"Computer-aided glaucoma diagnosis using stochastic watershed transformation on single fundus images","volume":"9","author":"Diaz-Pinto","year":"2019","journal-title":"J. Med. Imaging Heal. Inform."},{"key":"10.1016\/j.compmedimag.2025.102559_b14","doi-asserted-by":"crossref","first-page":"2675","DOI":"10.1016\/j.procs.2020.04.290","article-title":"Statistical parameters for glaucoma detection from color fundus images","volume":"171","author":"Elangovan","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.compmedimag.2025.102559_b15","series-title":"2020 IEEE International Conference on Image Processing","first-page":"2526","article-title":"Glaucoma detection from raw circumpapillary OCT images using fully convolutional neural networks","author":"Garc\u00eda","year":"2020"},{"key":"10.1016\/j.compmedimag.2025.102559_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105855","article-title":"Glaucoma detection from raw SD-OCT volumes: a novel approach focused on spatial dependencies","volume":"200","author":"Garc\u00eda","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.compmedimag.2025.102559_b17","first-page":"1","article-title":"Improved machine learning-based glaucoma detection from fundus images using texture features in FAWT and LS-SVM classifier","author":"Gautam","year":"2024","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.compmedimag.2025.102559_b18","series-title":"Mobile Computing and Sustainable Informatics: Proceedings of ICMCSI 2022","first-page":"869","article-title":"Glaucoma diagnosis: handcrafted features versus deep learning","author":"Giraddi","year":"2022"},{"key":"10.1016\/j.compmedimag.2025.102559_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117968","article-title":"Artificial Intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images","volume":"207","author":"Haider","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compmedimag.2025.102559_b20","series-title":"ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"961","article-title":"Multi-modal self-supervised pre-training for joint optic disc and cup segmentation in eye fundus images","author":"Hervella","year":"2020"},{"key":"10.1016\/j.compmedimag.2025.102559_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.108347","article-title":"End-to-end multi-task learning for simultaneous optic disc and cup segmentation and glaucoma classification in eye fundus images","volume":"116","author":"Hervella","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.compmedimag.2025.102559_b22","unstructured":"ICD-9-CM official guidelines for coding and reporting."},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102559_b23","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1109\/TBME.2019.2913211","article-title":"JointRCNN: a region-based convolutional neural network for optic disc and cup segmentation","volume":"67","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.compmedimag.2025.102559_b24","series-title":"2020 43rd International Conference on Telecommunications and Signal Processing","first-page":"533","article-title":"Efficient convolutional neural network based optic disc analysis using digital fundus images","author":"Joshi","year":"2020"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102559_b25","article-title":"Glaucoma detection using image processing and supervised learning for classification","volume":"2022","author":"Joshi","year":"2022","journal-title":"J. Heal. Eng."},{"key":"10.1016\/j.compmedimag.2025.102559_b26","series-title":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems","first-page":"489","article-title":"Optic disc and cup segmentation for glaucoma characterization using deep learning","author":"Kim","year":"2019"},{"key":"10.1016\/j.compmedimag.2025.102559_b27","first-page":"887","article-title":"Glaucoma is second leading cause of blindness globally","volume":"82","author":"Kingman","year":"2004","journal-title":"Bull. World Health Organ."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102559_b28","doi-asserted-by":"crossref","first-page":"7099","DOI":"10.1038\/s41598-019-43385-2","article-title":"Rim-to-disc ratio outperforms cup-to-disc ratio for glaucoma prescreening","volume":"9","author":"Kumar","year":"2019","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102559_b29","article-title":"Enhanced nature inspired-support vector machine for glaucoma detection","volume":"76","author":"Latif","year":"2023","journal-title":"Comput. Mater. Contin."},{"key":"10.1016\/j.compmedimag.2025.102559_b30","article-title":"Glaucoma detection from retinal fundus images using graph convolution based multi-task model","author":"Lenka","year":"2023","journal-title":"Authorea Prepr."},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102559_b31","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1109\/TMI.2019.2927226","article-title":"A large-scale database and a CNN model for attention-based glaucoma detection","volume":"39","author":"Li","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2025.102559_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107209","article-title":"TUNet and domain adaptation based learning for joint optic disc and cup segmentation","volume":"163","author":"Li","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compmedimag.2025.102559_b33","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part V 22","first-page":"521","article-title":"CFEA: Collaborative feature ensembling adaptation for domain adaptation in unsupervised optic disc and cup segmentation","author":"Liu","year":"2019"},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102559_b34","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1007\/s11548-021-02373-6","article-title":"Joint optic disc and optic cup segmentation based on boundary prior and adversarial learning","volume":"16","author":"Luo","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"10.1016\/j.compmedimag.2025.102559_b35","series-title":"Cnn-based approach for glaucoma diagnosis using transfer learning and lbp-based data augmentation","author":"Maheshwari","year":"2020"},{"key":"10.1016\/j.compmedimag.2025.102559_b36","first-page":"20","article-title":"Glaucoma: The silent thief of sight","volume":"110","author":"Marsden","year":"2014","journal-title":"Nurs. Times"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102559_b37","first-page":"1603","article-title":"Glaucoma disease detection using stacked attention U-Net and deep convolutional neural network","volume":"45","author":"Murugesan","year":"2023","journal-title":"J. Intell. Fuzzy Systems"},{"key":"10.1016\/j.compmedimag.2025.102559_b38","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.procs.2021.12.040","article-title":"Optic disc and cup segmentations for glaucoma assessment using cup-to-disc ratio","volume":"196","author":"Neto","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.compmedimag.2025.102559_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.101570","article-title":"Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs","volume":"59","author":"Orlando","year":"2020","journal-title":"Med. Image Anal."},{"issue":"11","key":"10.1016\/j.compmedimag.2025.102559_b40","doi-asserted-by":"crossref","first-page":"7958","DOI":"10.1080\/03772063.2022.2043783","article-title":"Glaucoma detection using optical coherence tomography images: a systematic review of clinical and automated studies","volume":"69","author":"Raja","year":"2023","journal-title":"IETE J. Res."},{"key":"10.1016\/j.compmedimag.2025.102559_b41","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1007\/s10278-020-00383-5","article-title":"Extraction of retinal layers through convolution neural network (CNN) in an OCT image for glaucoma diagnosis","volume":"33","author":"Raja","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"10.1016\/j.compmedimag.2025.102559_b42","series-title":"2022 2nd International Conference on Digital Futures and Transformative Technologies","first-page":"1","article-title":"A review of autonomous glaucomatous grading via OCT imagery","author":"Raja","year":"2022"},{"issue":"4","key":"10.1016\/j.compmedimag.2025.102559_b43","doi-asserted-by":"crossref","first-page":"335","DOI":"10.4258\/hir.2018.24.4.335","article-title":"Automated detection of retinal nerve fiber layer by texture-based analysis for glaucoma evaluation","volume":"24","author":"Septiarini","year":"2018","journal-title":"Heal. Inform. Res."},{"key":"10.1016\/j.compmedimag.2025.102559_b44","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1134\/S1054661817030269","article-title":"Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network","volume":"27","author":"Sevastopolsky","year":"2017","journal-title":"Pattern Recognit. Image Anal."},{"key":"10.1016\/j.compmedimag.2025.102559_b45","article-title":"Glaucoma detection in retinal fundus images using U-Net and supervised machine learning algorithms","volume":"5","author":"Shinde","year":"2021","journal-title":"Intell.-Based Med."},{"issue":"10","key":"10.1016\/j.compmedimag.2025.102559_b46","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.3390\/diagnostics13101738","article-title":"Automatic diagnosis of glaucoma from retinal images using deep learning approach","volume":"13","author":"Shoukat","year":"2023","journal-title":"Diagnostics"},{"issue":"10","key":"10.1016\/j.compmedimag.2025.102559_b47","doi-asserted-by":"crossref","first-page":"2426","DOI":"10.1093\/comjnl\/bxac093","article-title":"Glaucoma detection using multiple feature set with recurrent neural network","volume":"66","author":"Shyla","year":"2023","journal-title":"Comput. J."},{"key":"10.1016\/j.compmedimag.2025.102559_b48","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s11517-020-02307-5","article-title":"An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus","volume":"59","author":"Singh","year":"2021","journal-title":"Med. Biol. Eng. Comput."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102559_b49","first-page":"1004","article-title":"A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis","volume":"2","author":"Sivaswamy","year":"2015","journal-title":"JSM Biomed. Imaging Data Pap."},{"key":"10.1016\/j.compmedimag.2025.102559_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijleo.2023.170861","article-title":"A new convolution neural network model \u201cKR-NET\u201d for retinal fundus glaucoma classification","volume":"283","author":"Sonti","year":"2023","journal-title":"Optik"},{"key":"10.1016\/j.compmedimag.2025.102559_b51","first-page":"181","article-title":"The disc damage likelihood scale: reproducibility of a new method of estimating the amount of optic nerve damage caused by glaucoma","volume":"100","author":"Spaeth","year":"2002","journal-title":"Trans. Am. Ophthalmol. Soc."},{"issue":"14","key":"10.1016\/j.compmedimag.2025.102559_b52","doi-asserted-by":"crossref","first-page":"4916","DOI":"10.3390\/app10144916","article-title":"Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images","volume":"10","author":"Sreng","year":"2020","journal-title":"Appl. Sci."},{"key":"10.1016\/j.compmedimag.2025.102559_b53","series-title":"Soft Computing and Its Engineering Applications: Second International Conference, icSoftComp 2020, Changa, Anand, India, December 11\u201312, 2020, Proceedings","first-page":"316","article-title":"Glaucoma detection using features of optic nerve head, CDR and ISNT from fundus image of eye","volume":"Vol. 1374","author":"Sudhalkar","year":"2021"},{"key":"10.1016\/j.compmedimag.2025.102559_b54","series-title":"Computational Pathology and Ophthalmic Medical Image Analysis: First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings 5","first-page":"236","article-title":"Localizing optic disc and cup for glaucoma screening via deep object detection networks","author":"Sun","year":"2018"},{"issue":"10","key":"10.1016\/j.compmedimag.2025.102559_b55","doi-asserted-by":"crossref","first-page":"4668","DOI":"10.3390\/s23104668","article-title":"Identifying the edges of the optic cup and the optic disc in glaucoma patients by segmentation","volume":"23","author":"Tadisetty","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.compmedimag.2025.102559_b56","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.eswa.2019.03.009","article-title":"Optic disc and optic cup segmentation from retinal images using hybrid approach","volume":"127","author":"Thakur","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compmedimag.2025.102559_b57","article-title":"A deep neural network and machine learning approach for retinal fundus image classification","volume":"3","author":"Thanki","year":"2023","journal-title":"Heal. Anal."},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102559_b58","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1016\/j.bbe.2021.05.011","article-title":"Automated segmentation of optic disc and optic cup for glaucoma assessment using improved UNET++ architecture","volume":"41","author":"Tulsani","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"10.1016\/j.compmedimag.2025.102559_b59","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2023.1175881","article-title":"Multi-stage glaucoma classification using pre-trained convolutional neural networks and voting-based classifier fusion","volume":"14","author":"Velpula","year":"2023","journal-title":"Front. Physiol."},{"key":"10.1016\/j.compmedimag.2025.102559_b60","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2024.3412185","article-title":"Glaucoma identification using convolutional neural networks ensemble for optic disc and cup segmentation","author":"Virbukait\u0117","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.compmedimag.2025.102559_b61","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104906","article-title":"Towards an extended EfficientNet-based U-Net framework for joint optic disc and cup segmentation in the fundus image","volume":"85","author":"Wang","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"issue":"11","key":"10.1016\/j.compmedimag.2025.102559_b62","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1109\/TMI.2019.2899910","article-title":"Patch-based output space adversarial learning for joint optic disc and cup segmentation","volume":"38","author":"Wang","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"20","key":"10.1016\/j.compmedimag.2025.102559_b63","doi-asserted-by":"crossref","first-page":"4401","DOI":"10.3390\/s19204401","article-title":"Mixed maximum loss design for optic disc and optic cup segmentation with deep learning from imbalanced samples","volume":"19","author":"Xu","year":"2019","journal-title":"Sensors"},{"key":"10.1016\/j.compmedimag.2025.102559_b64","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compmedimag.2019.02.005","article-title":"Robust optic disc and cup segmentation with deep learning for glaucoma detection","volume":"74","author":"Yu","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"issue":"5","key":"10.1016\/j.compmedimag.2025.102559_b65","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"10.1016\/j.compmedimag.2025.102559_b66","series-title":"2010 Annual International Conference of the IEEE Engineering in Medicine and Biology","first-page":"3065","article-title":"Origa-light: An online retinal fundus image database for glaucoma analysis and research","author":"Zhang","year":"2010"},{"key":"10.1016\/j.compmedimag.2025.102559_b67","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106796","article-title":"Joint optic disc and cup segmentation based on elliptical-like morphological feature and spatial geometry constraint","volume":"158","author":"Zhao","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compmedimag.2025.102559_b68","article-title":"EARDS: EfficientNet and attention-based residual depth-wise separable convolution for joint OD and OC segmentation","volume":"17","author":"Zhou","year":"2023","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.compmedimag.2025.102559_b69","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.compmedimag.2016.07.012","article-title":"Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation","volume":"55","author":"Zilly","year":"2017","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2025.102559_b70","series-title":"2022 2nd International Conference of Smart Systems and Emerging Technologies","first-page":"1","article-title":"Automated grading of diabetic macular edema using color retinal photographs","author":"Zubair","year":"2022"},{"issue":"3","key":"10.1016\/j.compmedimag.2025.102559_b71","first-page":"109","article-title":"Automated segmentation of hard exudates using dynamic thresholding to detect diabetic retinopathy in retinal photographs","volume":"7","author":"Zubair","year":"2016","journal-title":"J. Multim. Process. Technol."},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102559_b72","first-page":"187","article-title":"Classification of diabetic macular edema and its stages using color fundus image","volume":"12","author":"Zubair","year":"2014","journal-title":"J. Electron. Sci. Technol."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102559_b73","doi-asserted-by":"crossref","first-page":"22533","DOI":"10.1038\/s41598-024-73823-9","article-title":"Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images","volume":"14","author":"Zubair","year":"2024","journal-title":"Sci. Rep."},{"issue":"8","key":"10.1016\/j.compmedimag.2025.102559_b74","doi-asserted-by":"crossref","DOI":"10.1016\/j.jksuci.2023.101719","article-title":"A comprehensive computer-aided system for an early-stage diagnosis and classification of diabetic macular edema","volume":"35","author":"Zubair","year":"2023","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"10.1016\/j.compmedimag.2025.102559_b75","series-title":"2013 IEEE International Conference on Imaging Systems and Techniques","first-page":"239","article-title":"Automated detection of optic disc for the analysis of retina using color fundus image","author":"Zubair","year":"2013"}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611125000680?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611125000680?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T20:09:34Z","timestamp":1777579774000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611125000680"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":75,"alternative-id":["S0895611125000680"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2025.102559","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhanced glaucoma classification through advanced segmentation by integrating cup-to-disc ratio and neuro-retinal rim features","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2025.102559","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"102559"}}