{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T21:43:09Z","timestamp":1771105389594,"version":"3.50.1"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","funder":[{"DOI":"10.13039\/501100004587","name":"Instituto de Salud Carlos III","doi-asserted-by":"publisher","award":["PI21\/00064"],"award-info":[{"award-number":["PI21\/00064"]}],"id":[{"id":"10.13039\/501100004587","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004587","name":"Instituto de Salud Carlos III","doi-asserted-by":"publisher","award":["PI18\/00169"],"award-info":[{"award-number":["PI18\/00169"]}],"id":[{"id":"10.13039\/501100004587","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007512","name":"Universitat Rovira i Virgili","doi-asserted-by":"publisher","award":["2020PFR-B2-61"],"award-info":[{"award-number":["2020PFR-B2-61"]}],"id":[{"id":"10.13039\/501100007512","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:p> The early diagnosis of the glaucoma disease in the eye is crucial to avoid vision loss. This paper proposes an efficient computer-aided detection (CAD) system for diagnosing glaucoma based on fundus images, deep transfer learning and fuzzy aggregation operators. Specifically, the proposed CAD system includes three stages: (1) Detection of the region of interest of the optic disc using an efficient deep learning network, (2) Classification of images based on different pre-trained deep convolutional neural networks and support vector machines, and (3) Use of fuzzy aggregation operators to fuse the predictions of glaucoma classifiers. We used three popular yet robust aggregators: ordered weighted averaging (OWA) operator, weighted power mean (WPM), and exponential mean (EXM). We assessed the efficacy of the proposed glaucoma CAD system on three public datasets: DRISHTI-GS1, RIM-ONE, and REFUGE. The proposed conjunctive OWA aggregation method (Conj-OWA) achieves the best glaucoma classification results. Specifically, it achieves accuracy values of 90.2%, 97.8%, and 94.3% and area under the curve (AUC) values of 95.3%, 99.8%, and 96.2%, respectively, on DRISHTI-GS1, RIM-ONE, and REFUGE databases. <\/jats:p>","DOI":"10.1142\/s0218213023400018","type":"journal-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T15:52:32Z","timestamp":1668786752000},"source":"Crossref","is-referenced-by-count":3,"title":["Glaucoma Detection in Retinal Fundus Images Based on Deep Transfer Learning and Fuzzy Aggregation Operators"],"prefix":"10.1142","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2639-0177","authenticated-orcid":false,"given":"Mohammed Yousef Salem","family":"Ali","sequence":"first","affiliation":[{"name":"Departament Enginyeria Inform\u00e0tica i Matem\u00e0tiques, Universitat Rovira i Virgili, Tarragona 43007, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Jabreel","sequence":"additional","affiliation":[{"name":"Gaist Solutions Ltd., Skipton, England"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aida","family":"Valls","sequence":"additional","affiliation":[{"name":"Departament Enginyeria Inform\u00e0tica i Matem\u00e0tiques, Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Baget","sequence":"additional","affiliation":[{"name":"Ophthalmology Service, Hospital Universitari Sant Joan, Institut de Investigaci\u00f3 Sanit\u00e0ria Pere Virgili, and Universitat Rovira i Virgili, 43204 Reus, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Abdel-Nasser","sequence":"additional","affiliation":[{"name":"Departament Enginyeria Inform\u00e0tica i Matem\u00e0tiques, Universitat Rovira i Virgili, Tarragona 43007, Spain"},{"name":"Department of Electrical Engineering, Aswan University, 81528 Aswan, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2023,4,5]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213023400018","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T11:44:44Z","timestamp":1680695084000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218213023400018"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3]]},"references-count":0,"journal-issue":{"issue":"02","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["10.1142\/S0218213023400018"],"URL":"https:\/\/doi.org\/10.1142\/s0218213023400018","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3]]},"article-number":"2340001"}}