Glaucoma disease diagnosis with an artificial algae-based deep learning algorithm

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dc.contributor.authorIbrahim, Mohammed H.
dc.contributor.authorHacibeyoglu, Mehmet
dc.contributor.authorAgaoglu, Afsin
dc.contributor.authorUcar, Fikret
dc.date.accessioned2024-02-23T13:59:34Z
dc.date.available2024-02-23T13:59:34Z
dc.date.issued2022
dc.departmentNEÜen_US
dc.description.abstractGlaucoma disease is optic neuropathy; in glaucoma, the optic nerve is damaged because the long duration of intraocular pressure can be caused blindness. Nowadays, deep learning classification algorithms are widely used to diagnose various diseases. However, in general, the training of deep learning algorithms is carried out by traditional gradient-based learning techniques that converge slowly and are highly likely to fall to the local minimum. In this study, we proposed a novel decision support system based on deep learning to diagnose glaucoma. The proposed system has two stages. In the first stage, the preprocessing of glaucoma disease data is performed by normalization and mean absolute deviation method, and in the second stage, the training of the deep learning is made by the artificial algae optimization algorithm. The proposed system is compared to traditional gradient-based deep learning and deep learning trained with other optimization algorithms like genetic algorithm, particle swarm optimization, bat algorithm, salp swarm algorithm, and equilibrium optimizer. Furthermore, the proposed system is compared to the state-of-the-art algorithms proposed for the glaucoma detection. The proposed system has outperformed other algorithms in terms of classification accuracy, recall, precision, false positive rate, and F1-measure by 0.9815, 0.9795, 0.9835, 0.0165, and 0.9815, respectively.en_US
dc.identifier.doi10.1007/s11517-022-02510-6
dc.identifier.endpage796en_US
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.issue3en_US
dc.identifier.pmid35080695en_US
dc.identifier.scopus2-s2.0-85123591079en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage785en_US
dc.identifier.urihttps://doi.org/10.1007/s11517-022-02510-6
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11246
dc.identifier.volume60en_US
dc.identifier.wosWOS:000748671800001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofMedical & Biological Engineering & Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectDecision Supporten_US
dc.subjectDeep Learningen_US
dc.subjectDiagnosisen_US
dc.subjectGlaucoma Diseaseen_US
dc.titleGlaucoma disease diagnosis with an artificial algae-based deep learning algorithmen_US
dc.typeArticleen_US

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