Automatic detection of keratoconus on Pentacam images using feature selection based on deep learning

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dc.authoridFirat, Murat/0000-0001-6040-9332
dc.authoridTuncer, Taner/0000-0003-0526-4526
dc.authorwosidCankaya, Cem/HTR-3803-2023
dc.authorwosidTuncer, Taner/W-4789-2018
dc.authorwosidCINAR, Ahmet/W-5792-2018
dc.contributor.authorFirat, Murat
dc.contributor.authorCankaya, Cem
dc.contributor.authorCinar, Ahmet
dc.contributor.authorTuncer, Taner
dc.date.accessioned2024-08-04T20:51:40Z
dc.date.available2024-08-04T20:51:40Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractToday, corneal refraction, height, and thickness data, which are required in the diagnosis of keratoconus, can be obtained with corneal tomography devices. Pentacam four map display presenting this data is one of the most basic options in the diagnosis of keratoconus. In this article, an artificial intelligence-based method using Pentacam images is proposed to distinguish keratoconus from healthy eyes. Axial/sagittal curvature, back elevation, front elevation, and corneal thickness map images of a total of 341 keratoconus and 341 healthy corneas obtained from Inonu University ophthalmology clinic as the data set were given as input to AlexNet, one of the deep learning models, and the feature vectors of each image were obtained and combined. The most effective features in the determination of keratoconus were determined by applying ReliefF, minimum-redundancy-maximum-relevance (mRMR) and Laplacian algorithms, which are widely used in feature extraction algorithms, to the obtained feature vector. These features are classified using the support vector machine (SVM) classifier, which has high performance in binary classification. The accuracy, specificity, and sensitivity of keratoconus detection with the proposed method were found to be 98.53%, 99.01%, and 98.06%, respectively. The developed model can support the clinician to evaluate the features of the cornea and to detect keratoconus, which is difficult through subjective assessments, especially in the subclinical and early stages of the disease.en_US
dc.identifier.doi10.1002/ima.22717
dc.identifier.endpage1560en_US
dc.identifier.issn0899-9457
dc.identifier.issn1098-1098
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85124557095en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1548en_US
dc.identifier.urihttps://doi.org/10.1002/ima.22717
dc.identifier.urihttps://hdl.handle.net/11616/100483
dc.identifier.volume32en_US
dc.identifier.wosWOS:000754260000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofInternational Journal of Imaging Systems and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectdeep learningen_US
dc.subjectfeature selectionen_US
dc.subjectkeratoconusen_US
dc.subjectPentacam four maps refractiveen_US
dc.titleAutomatic detection of keratoconus on Pentacam images using feature selection based on deep learningen_US
dc.typeArticleen_US

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