Pseudopapilledema diagnosis based on a hybrid approach using deep transfer learning

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dc.contributor.authorAl-Azzawi, Athar
dc.contributor.authorAl-Jumaili, Saif
dc.contributor.authorDuru, Adil Deniz
dc.contributor.authorBayat, Oğuz
dc.contributor.authorKurnaz, Sefer
dc.contributor.authorUçan, Osman Nuri
dc.date.accessioned2023-12-21T09:10:59Z
dc.date.available2023-12-21T09:10:59Z
dc.date.issued2023en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractThis Papilledema is edema caused by elevated pressure inside the brain near the area that leads the optic nerve to reach the eye. If left untreated, this condition can cause severe difficulties, for instance, aberrant optical changes, reduced sharpness of vision, and irreversible blindness. At present, an approach based on image processing for determining the degree of papilledema from color fundus images was given utilizing transfer learning approaches. The used dataset here contains 295 papilledema images, 295 pseudopapilledema images, and 779 control images. For the image preparation, a segmentation optimizer was utilized. The performance of the transfer learning techniques GoogleNet, MobileNetV2, ResNet-18, and ResNet-50 was then compared. Furthermore, Sensitivity and specificity and constructed ROC curves were calculated. The ResNet-50 employing the optimizer ADAM method performed best in the testing, with 98% total accuracy. The findings of the studies demonstrated that a combination of segmentation, optimization models, and transfer learning techniques may be utilized to determine the severity of papilledema automatically. The total accuracy was higher when compared to other similar studies described in the literature.en_US
dc.identifier.citationAl-azzawi, A., Al-jumaili, S., Duru, A. D., Bayat, O., Kurnaz, S., & Uçan, O. N. (2023, October). Pseudopapilledema diagnosis based on a hybrid approach using deep transfer learning. In 2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-6). IEEE.en_US
dc.identifier.isbn9798350342154
dc.identifier.scopus2-s2.0-85179139481
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4402
dc.indekslendigikaynakScopus
dc.institutionauthorAl-Azzawi, Athar
dc.institutionauthorAl-Jumaili, Saif
dc.institutionauthorBayat, Oğuz
dc.institutionauthorKurnaz, Sefer
dc.institutionauthorUçan, Osman Nuri
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023 - Proceedings
dc.relation.isversionof10.1109/ISMSIT58785.2023.10304843en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGoogleneten_US
dc.subjectMobilenetv2en_US
dc.subjectPapilledemaen_US
dc.subjectPseudopapilledemaen_US
dc.subjectResnet-18en_US
dc.subjectResnet-50en_US
dc.titlePseudopapilledema diagnosis based on a hybrid approach using deep transfer learning
dc.typeConference Object

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