Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image

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dc.authoridMajidpour, Jafar/0000-0002-5828-411X
dc.authoridKais, samer/0000-0003-2236-9303
dc.authoridSalih, Sinan/0000-0003-0717-7506
dc.authoridRashid, Tarik A/0000-0002-8661-258X
dc.authoridJosephNg, Poh Soon/0000-0002-6240-652X
dc.contributor.authorJameel, Samer Kais
dc.contributor.authorAydin, Sezgin
dc.contributor.authorGhaeb, Nebras H.
dc.contributor.authorMajidpour, Jafar
dc.contributor.authorRashid, Tarik A.
dc.contributor.authorSalih, Sinan Q.
dc.contributor.authorJosephNg, Poh Soon
dc.date.accessioned2025-03-17T12:25:17Z
dc.date.available2025-03-17T12:25:17Z
dc.date.issued2022
dc.departmentTarsus Üniversitesi
dc.description.abstractCorneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients.
dc.identifier.doi10.3390/biom12121888
dc.identifier.issn2218-273X
dc.identifier.issue12
dc.identifier.pmid36551316
dc.identifier.scopus2-s2.0-85144578547
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/biom12121888
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1597
dc.identifier.volume12
dc.identifier.wosWOS:000902257500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofBiomolecules
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250316
dc.subjectconditional generative adversarial networks
dc.subjecttransfer learning
dc.subjectsynthesize images
dc.subjectcorneal diseases
dc.subjectdata augmentation
dc.titleExploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image
dc.typeArticle

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