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A Hybrid Artificial Intelligence Model for Detecting Keratoconus

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dc.contributor.authorAlyasseri Z.A.A.en_US
dc.contributor.authorAl-Timemy A.H.en_US
dc.contributor.authorAbasi A.K.en_US
dc.contributor.authorLavric A.en_US
dc.contributor.authorMohammed H.J.en_US
dc.contributor.authorTakahashi H.en_US
dc.contributor.authorMilhomens Filho J.A.en_US
dc.contributor.authorCampos M.en_US
dc.contributor.authorHazarbassanov R.M.en_US
dc.contributor.authorYousefi S.en_US
dc.contributor.authorid57862594800en_US
dc.contributor.authorid35752795500en_US
dc.contributor.authorid57208488241en_US
dc.contributor.authorid55308321800en_US
dc.contributor.authorid57202657688en_US
dc.contributor.authorid57204099946en_US
dc.contributor.authorid57221544458en_US
dc.contributor.authorid35613019600en_US
dc.contributor.authorid6508322463en_US
dc.contributor.authorid36986998500en_US
dc.date.accessioned2023-05-29T09:35:57Z
dc.date.available2023-05-29T09:35:57Z
dc.date.issued2022
dc.description.abstractMachine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 and 579 KCN4) from Department of Ophthalmology and Visual Sciences, Paulista Medical School, Federal University of S�o Paulo, S�o Paulo in Brazil and 1531 eyes (Healthy = 400, KCN1 = 378, KCN2 = 285, KCN3 = 200, KCN4 = 88) from Department of Ophthalmology, Jichi Medical University, Tochigi in Japan and used several accuracy metrics including Precision, Recall, F-Score, and Purity. We compared the proposed method with three other standard unsupervised algorithms including k-means, Kmedoids, and Spectral cluster. Based on two independent datasets, the proposed model outperformed the other algorithms, and thus could provide improved identification of the corneal status of the patients with keratoconus. � 2022 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12979
dc.identifier.doi10.3390/app122412979
dc.identifier.issue24
dc.identifier.scopus2-s2.0-85144890277
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85144890277&doi=10.3390%2fapp122412979&partnerID=40&md5=18280399352ef2d0a43a7e503a3516d4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26627
dc.identifier.volume12
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleApplied Sciences (Switzerland)
dc.titleA Hybrid Artificial Intelligence Model for Detecting Keratoconusen_US
dc.typeArticleen_US
dspace.entity.typePublication
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