{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T22:45:54Z","timestamp":1768689954695,"version":"3.49.0"},"reference-count":53,"publisher":"Tech Science Press","issue":"1","license":[{"start":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T00:00:00Z","timestamp":1721347200000},"content-version":"vor","delay-in-days":200,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2024]]},"DOI":"10.32604\/cmc.2024.048987","type":"journal-article","created":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T09:09:09Z","timestamp":1720429749000},"page":"1381-1405","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":3,"title":["Refined Anam-Net: Lightweight Deep Learning Model for Improved Segmentation Performance of Optic Cup and Disc for Glaucoma Diagnosis"],"prefix":"10.32604","volume":"80","author":[{"given":"Khursheed","family":"Aurangzeb","sequence":"first","affiliation":[]},{"given":"Syed Irtaza","family":"Haider","sequence":"additional","affiliation":[]},{"given":"Musaed","family":"Alhussein","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2024]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"164344","DOI":"10.1109\/ACCESS.2019.2953259","article-title":"An improved retinal vessel segmentation framework using Frangi filter coupled with the probabilistic patch based denoiser","volume":"7","author":"Khawaja","year":"2019","journal-title":"IEEE Access"},{"key":"ref2","first-page":"1","article-title":"A morphological hessian based approach for retinal blood vessels segmentation and denoising using region based otsu thresholding","volume":"11","author":"Bahadar","year":"Jul. 2016","journal-title":"PLoS One"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"47930","DOI":"10.1109\/ACCESS.2021.3068477","article-title":"Contrast enhancement of fundus images by employing modified PSO for improving the performance of deep learning models","volume":"9","author":"Aurangzeb","year":"2021","journal-title":"IEEE Access"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1136\/bjo.2005.081224","article-title":"The number of people with glaucoma worldwide in 2010 and 2020","volume":"90","author":"Quigley","year":"Mar. 2006","journal-title":"Br. J. Ophthalmol."},{"key":"ref5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2018.07.012","article-title":"Computer-aided diagnosis of glaucoma using fundus images: A review","volume":"165","author":"Hagiwara","year":"Oct. 2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref6","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s12026-016-8837-3","article-title":"Glaucoma: Recent advances in the involvement of autoimmunity","volume":"65","author":"Rizzo","year":"Feb. 2017","journal-title":"Immunol. Res."},{"key":"ref7","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.ins.2018.01.051","article-title":"Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images","volume":"441","author":"Raghavendra","year":"May 2018","journal-title":"Inf. Sci."},{"key":"ref8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/180972","article-title":"Optic disc and optic cup segmentation methodologies for glaucoma image detection: A survey","volume":"2015","author":"Almazroa","year":"2015","journal-title":"J. Ophthalmol."},{"key":"ref9","series-title":"2017 1st Int. Conf. Next Gen. Comput. Appl. (NextComp)","first-page":"157","article-title":"Clinical and technical perspective of glaucoma detection using oct and fundus images: A review","author":"Naveed","year":"2017"},{"key":"ref10","series-title":"6th Int. Conf. Inform. Commun. Technol. Embed. Syst. (IC-ICTES)","first-page":"1","article-title":"Glaucoma screening using rim width based on isnt rule","author":"Ruengkitpinyo","year":"2015"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bspc.2019.01.003","article-title":"An automated glaucoma screening system using cup-to-disc ratio via simple linear iterative clustering superpixel approach","volume":"53","author":"Mohamed","year":"Aug. 2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.ijmedinf.2017.11.015","article-title":"An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection","volume":"110","author":"Soorya","year":"Feb. 2018","journal-title":"Int. J. Med. Inform."},{"key":"ref13","series-title":"25th IEEE Int. Conf. Image Process. (ICIP)","first-page":"2227","article-title":"Automatic optic disk and cup segmentation of fundus images using deep learning","author":"Edupuganti","year":"2018"},{"key":"ref14","series-title":"IEEE 3rd Inform. Technol., Netw., Electron. Autom. Control Conf. (ITNEC)","first-page":"1835","article-title":"Optic disc and cup segmentation based on deep learning","author":"Qin","year":"2019"},{"key":"ref15","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":"Jan. 2017","journal-title":"Comput. Med. Imaging Graph"},{"key":"ref16","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":"Jun. 2019","journal-title":"Comput. Med. Imaging Graph"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"1","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":"ref18","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"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"64483","DOI":"10.1109\/ACCESS.2019.2917508","article-title":"Optic disc and cup segmentation based on deep convolutional generative adversarial networks","volume":"7","author":"Jiang","year":"2019","journal-title":"IEEE Access"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1109\/TMI.2018.2791488","article-title":"Joint optic disc and cup segmentation based on multi-label deep network and polar transformation","volume":"37","author":"Fu","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1109\/TMI.2018.2837012","article-title":"Disc-aware ensemble network for glaucoma screening from fundus image","volume":"37","author":"Fu","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref22","first-page":"234","volume":"9351","author":"Ronneberger","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref24","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1109\/TNNLS.2021.3054746","article-title":"Anam-Net: Anamorphic depth embedding-based lightweight CNN for segmentation of anomalies in COVID-19 chest CT images","volume":"32","author":"Paluru","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref25","series-title":"4th Int. Work Conf. Bioinspired Intell. (IWOBI)","first-page":"139","article-title":"A novel approach to detect glaucoma in retinal fundus images using cup-disk and rim-disk ratio","author":"Agarwal","year":"2015"},{"key":"ref26","series-title":"2nd Int. Conf. Signal Process. Integr. Netw. (SPIN)","first-page":"143","article-title":"An adaptive threshold based algorithm for optic disc and cup segmentation in fundus images","author":"Issac","year":"2015"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1049\/iet-ipr.2016.0812","article-title":"Improved automated detection of glaucoma from fundus image using hybrid structural and textural features","volume":"11","author":"Khalil","year":"2017","journal-title":"IET Image Process."},{"key":"ref28","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.bspc.2018.01.014","article-title":"Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma","volume":"42","author":"Thakur","year":"Apr. 2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref29","series-title":"IEEE Int. Symp. Biomed. Imaging: From Nano to Macro","first-page":"948","article-title":"Optic disk and cup boundary detection using regional information","author":"Joshi","year":"2010"},{"key":"ref30","series-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc.","first-page":"6224","article-title":"Automatic optic disc segmentation with peripapillary atrophy elimination","author":"Cheng","year":"2011"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/TMI.2011.2106509","article-title":"Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment","volume":"30","author":"Joshi","year":"2011","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref32","first-page":"1","article-title":"A novel adaptive deformable model for automated optic disc and cup segmentation to aid glaucoma diagnosis","volume":"42","author":"Haleem","year":"Dec. 2017","journal-title":"J. Med. Syst."},{"key":"ref33","series-title":"IEEE Glob. Conf. Signal Inform. Process. (GlobalSIP)","first-page":"225","article-title":"Active discs for automated optic disc segmentation","author":"Kumar","year":"2015"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1109\/TMI.2013.2247770","article-title":"Superpixel classification based optic disc and optic cup segmentation for glaucoma screening","volume":"32","author":"Cheng","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref35","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":"Healthc Inform. Res."},{"key":"ref36","doi-asserted-by":"crossref","first-page":"53","DOI":"10.4258\/hir.2018.24.1.53","article-title":"Automatic glaucoma detection method applying a statistical approach to fundus images","volume":"24","author":"Septiarini","year":"2018","journal-title":"Healthc Inform. Res."},{"key":"ref37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/6814791","article-title":"Automatic detection of optic disc in retinal image by using keypoint detection, texture analysis, and visual dictionary techniques","volume":"2016","author":"Akyol","year":"2016","journal-title":"Comput. Math. Methods Med."},{"key":"ref38","series-title":"38th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC)","first-page":"3260","article-title":"Segmentation of optic disc and optic cup in retinal fundus images using shape regression","author":"Sedai","year":"2016"},{"key":"ref39","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":"Jul. 2017","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref40","series-title":"30th IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"6230","article-title":"Pyramid scene parsing network","author":"Zhao","year":"2017"},{"key":"ref41","series-title":"IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"ref42","unstructured":"L. C. Chen, G. Papandreou, F. Schroff, and H. Adam, \u201cRethinking atrous convolution for semantic image segmentation,\u201d arXiv:1706.05587, 2017."},{"key":"ref43","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s10278-018-0126-3","article-title":"Towards accurate segmentation of retinal vessels and the optic disc in fundoscopic images with generative adversarial network","volume":"32","author":"Son","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"ref44","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"CE-Net: Context encoder network for 2D medical image segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1007\/s11760-020-01815-z","article-title":"Application of an attention U-Net incorporating transfer learning for optic disc and cup segmentation","volume":"15","author":"Zhao","year":"2021","journal-title":"Signal, Image Video Process."},{"key":"ref46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42979-021-00491-1","article-title":"GlaucoNet: Patch based residual deep learning network for optic disc and cup segmentation towards glaucoma assessment","volume":"2","author":"Panda","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref47","first-page":"479","volume":"1373","author":"Chowdhury","year":"2021","journal-title":"Advanced Computational Paradigms and Hybrid Intelligent Computing. Advances in Intelligent Systems and Computing"},{"key":"ref48","doi-asserted-by":"crossref","first-page":"105069","DOI":"10.1109\/ACCESS.2023.3317348","article-title":"Systematic development of AI-enabled diagnostic systems for glaucoma and diabetic retinopathy","volume":"11","author":"Aurangzeb","year":"2023","journal-title":"IEEE Access"},{"key":"ref49","doi-asserted-by":"crossref","first-page":"2476","DOI":"10.1109\/TII.2020.3000204","article-title":"Optic disk and cup segmentation through fuzzy broad learning system for glaucoma screening","volume":"17","author":"Ali","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref50","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":"ref51","doi-asserted-by":"crossref","first-page":"3008","DOI":"10.1109\/TMI.2020.2983721","article-title":"CPFNet: Context pyramid fusion network for medical image segmentation","volume":"39","author":"Feng","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref52","doi-asserted-by":"crossref","first-page":"101874","DOI":"10.1016\/j.media.2020.101874","article-title":"CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging","volume":"67","author":"Mou","year":"2021","journal-title":"Med. Imaging Anal."},{"key":"ref53","doi-asserted-by":"crossref","first-page":"490","DOI":"10.3389\/fnhum.2022.1043569","article-title":"Convolutional autoencoder joint boundary and mask adversarial learning for fundus image segmentation","volume":"16","author":"Zhang","year":"2022","journal-title":"Front. Hum. Neurosci."}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.techscience.com\/files\/cmc\/2024\/TSP_CMC-80-1\/TSP_CMC_48987\/TSP_CMC_48987.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T11:29:16Z","timestamp":1741260556000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v80n1\/57356"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024]]},"published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2024.048987","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2023-12-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-22","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-07-18","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}