Sex Prediction of Hyoid Bone from Computed Tomography Images Using the DenseNet121 Deep Learning Model

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dc.contributor.authorBakici, Rukiye Sumeyye
dc.contributor.authorCakmak, Muhammet
dc.contributor.authorOner, Zulal
dc.contributor.authorOner, Serkan
dc.date.accessioned2024-09-29T16:12:26Z
dc.date.available2024-09-29T16:12:26Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description13th International Hippocrates Congress on Medical and Health Sciences -- DEC 15-16, 2023 -- TURKEYen_US
dc.description.abstractThe study aims to demonstrate the success of deep learning methods in sex prediction using hyoid bone. The images of people aged 15-94 years who underwent neck Computed Tomography (CT) were retrospectively scanned in the study. The neck CT images of the individuals were cleaned using the RadiAnt DICOM Viewer (version 2023.1) program, leaving only the hyoid bone. A total of 7 images in the anterior, posterior, superior, inferior, right, left, and right-anterior-upward directions were obtained from a patient's cut hyoid bone image. 2170 images were obtained from 310 hyoid bones of males, and 1820 images from 260 hyoid bones of females. 3990 images were completed to 5000 images by data enrichment. The dataset was divided into 80 % for training, 10 % for testing, and another 10 % for validation. It was compared with deep learning models DenseNet121, ResNet152, and VGG19. An accuracy rate of 87 % was achieved in the ResNet152 model and 80.2 % in the VGG19 model. The highest rate among the classified models was 89 % in the DenseNet121 model. This model had a specificity of 0.87, a sensitivity of 0.90, an F1 score of 0.89 in women, a specificity of 0.90, a sensitivity of 0.87, and an F1 score of 0.88 in men. It was observed that sex could be predicted from the hyoid bone using deep learning methods DenseNet121, ResNet152, and VGG19. Thus, a method that had not been tried on this bone before was used. This study also brings us one step closer to strengthening and perfecting the use of technologies, which will reduce the subjectivity of the methods and support the expert in the decision-making process of sex prediction.en_US
dc.identifier.endpage832en_US
dc.identifier.issn0717-9502
dc.identifier.issn0717-9367
dc.identifier.issue3en_US
dc.identifier.startpage826en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8762
dc.identifier.volume42en_US
dc.identifier.wosWOS:001289105800038en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherSoc Chilena Anatomiaen_US
dc.relation.ispartofInternational Journal of Morphologyen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHyoid boneen_US
dc.subjectDeep learningen_US
dc.subjectSex estimationen_US
dc.subjectDenseNet121en_US
dc.subjectResNet152en_US
dc.subjectVGG19en_US
dc.titleSex Prediction of Hyoid Bone from Computed Tomography Images Using the DenseNet121 Deep Learning Modelen_US
dc.typeConference Objecten_US

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