{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T04:52:06Z","timestamp":1774673526485,"version":"3.50.1"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T00:00:00Z","timestamp":1658534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>One of the most widespread illnesses of blindness is glaucoma. Optic nerve are essentialfor clear vision, but glaucoma effects the optic nerves and results blurred vision. This condition is often exacerbated by abnormally high intra-ocular pressure. Accurate early identification and continuous screening can help to minimize loss of vision. A non-invasive computer-aided diagnosis treatment uses optical fundus images to detect glaucoma in its early stages. This work includes image preprocessing, optic disk (OD) segmentation, feature extraction from the OD and recurrent neural network classification to identify glaucoma. The performance of the proposed system is tested using fundus image datasets such as DRISHTI-GS and Large-Scale Attention-Based Glaucoma (LAG). By this method, glaucoma detection accuracyof 96.1% is obtained for DRISHTI-GS and 92.73% for LAG dataset, which is higher thanthe existing state of arts. Proposed procedure can help ophthalmologists diagnose glaucomawith good performance.<\/jats:p>","DOI":"10.1093\/comjnl\/bxac093","type":"journal-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T23:18:41Z","timestamp":1658531921000},"page":"2426-2436","source":"Crossref","is-referenced-by-count":5,"title":["Glaucoma Detection Using Multiple Feature Set With Recurrent Neural Network"],"prefix":"10.1093","volume":"66","author":[{"given":"N S Jeya","family":"Shyla","sequence":"first","affiliation":[{"name":"Department of Computer Science and Research Centre, Nesamony Memorial Christian College, Marthandam, Affiliated to Manonmaniam Sundaranar University , Tirunelveli 627 012, TamilNadu , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7728-1910","authenticated-orcid":false,"given":"W R Sam","family":"Emmanuel","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Research Centre, Nesamony Memorial Christian College, Marthandam, Affiliated to Manonmaniam Sundaranar University , Tirunelveli 627 012, TamilNadu , India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,7,23]]},"reference":[{"key":"2023101421211448000_ref1","first-page":"53","article-title":"DRISHTI-GS: retinal image dataset for optic nerve head ( ONH ) segmentation IIIT, Hyderabad, India","author":"Sivaswamy","year":"2014"},{"key":"2023101421211448000_ref2","first-page":"1","article-title":"A large-scale database and CNN model","author":"Li","year":"2019"},{"key":"2023101421211448000_ref3","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":"2018","journal-title":"Inf. Sci. (Ny)."},{"key":"2023101421211448000_ref4","first-page":"1","article-title":"Transfer learning for early and advanced glaucoma detection with convolutional neural networks","author":"Serener","year":"2019","journal-title":"TIPTEKNO 2019 - Tip Teknol. Kongresi"},{"key":"2023101421211448000_ref5","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1007\/s10384-019-00659-6","article-title":"Evaluation of deep convolutional neural networks for glaucoma detection","volume":"63","author":"Phan","year":"2019","journal-title":"Jpn. J. Ophthalmol."},{"key":"2023101421211448000_ref6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1167\/tvst.9.2.27","article-title":"Effects of study population, labeling and training on glaucoma detection using deep learning algorithms","volume":"9","author":"Christopher","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"2023101421211448000_ref7","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1049\/iet-ipr.2018.5297","article-title":"Computer aided diagnosis of glaucoma using discrete and empirical wavelet transform from fundus images","volume":"13","author":"Kirar","year":"2019","journal-title":"IET Image Process."},{"key":"2023101421211448000_ref8","doi-asserted-by":"crossref","DOI":"10.1038\/s41746-020-00329-9","article-title":"Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection","volume":"3","author":"Li","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"2023101421211448000_ref9","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. 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Imaging Graph."},{"key":"2023101421211448000_ref18","doi-asserted-by":"crossref","first-page":"4845","DOI":"10.1109\/ACCESS.2018.2790040","article-title":"A correction to the article fast optic disc segmentation in retina using polar transform","volume":"6","author":"Zahoor","year":"2018","journal-title":"IEEE Access"},{"key":"2023101421211448000_ref19","first-page":"1","article-title":"Current research on glaucoma detection using compact variational mode decomposition from fundus images","volume":"12","author":"Kirar","year":"2019","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"2023101421211448000_ref20","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.eswa.2018.06.010","article-title":"Convolutional neural network and texture descriptor-based automatic detection and diagnosis of glaucoma","volume":"110","author":"Santos Ferreira","year":"2018","journal-title":"Expert Syst. 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