A CNN-Based Novel Approach for Classification of Sacral Hiatus with GAN- Powered Tabular Data Set

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dc.authoridKILIÇ, Ferhat/0000-0003-2006-4098
dc.authoridER, Orhan/0000-0002-4732-9490
dc.authorwosidKILIÇ, Ferhat/AAE-9998-2019
dc.contributor.authorKilic, Ferhat
dc.contributor.authorKorkmaz, Murat
dc.contributor.authorEr, Orhan
dc.contributor.authorAltin, Cemil
dc.date.accessioned2024-03-09T18:48:52Z
dc.date.available2024-03-09T18:48:52Z
dc.date.issued2023
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractepidural anaesthesia is usually the most well-known technique in obstetrics to deal with chronic back pain. Due to variations in the shape and size of the sacral hiatus (SH), its classification is a crucial and challenging task. Clinically, it is required in trauma, where surgeons must make fast and correct selections. Past studies have focused on morphometric and statistical analysis to classify it. Therefore, it is vital to automatically and accurately classify SH types through deep learning methods. To this end, we proposed the Multi-Task Process (MTP), a novel classification approach to classify the SH MTP that initially uses a small medical tabular data set obtained by manual feature extraction on computed tomography scans of the sacrums. Second, it augments the data set synthetically through a Generative Adversarial Network (GAN). In addition, it adapts a two-dimensional (2D) embedding algorithm to convert tabular features into images. Finally, it feeds images into Convolutional Neural Networks (CNNs). The application of MTP to six CNN models achieved remarkable classification success rates of approximately 90 % to 93 %. The proposed MTP approach eliminates the small medical tabular data problem that results in bone classification on deep models.en_US
dc.identifier.doi10.5755/j02.eie.33852
dc.identifier.endpage53en_US
dc.identifier.issn1392-1215
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85163981142en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage44en_US
dc.identifier.urihttps://doi.org/10.5755/j02.eie.33852
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1514
dc.identifier.volume29en_US
dc.identifier.wosWOS:000999128100006en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherKaunas Univ Technologyen_US
dc.relation.ispartofElektronika Ir Elektrotechnikaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBone Classification; Synthetic Tabular Data Generation; Two-Dimensional Embedding; Transfer Learning; Generative Adversarial Networks; Convolutional Neural Networks; Deep Learningen_US
dc.titleA CNN-Based Novel Approach for Classification of Sacral Hiatus with GAN- Powered Tabular Data Seten_US
dc.typeArticleen_US

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