GENDER IDENTIFICATION FROM LEFT HAND-WRIST X-RAY IMAGES WITH A HYBRID DEEP LEARNING METHOD

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dc.authoridOZDEMIR, Cuneyt/0000-0002-9252-5888
dc.contributor.authorOzdemir, Cuneyt
dc.contributor.authorGedik, Mehmet Ali
dc.contributor.authorKucuker, Hudaverdi
dc.contributor.authorKaya, Yilmaz
dc.date.accessioned2024-12-24T19:33:51Z
dc.date.available2024-12-24T19:33:51Z
dc.date.issued2023
dc.departmentSiirt Üniversitesi
dc.description.abstractIn forensic investigations, characteristics such as gender, age, ethnic origin, and height are important in determining biological identity. In this study, we developed a deep learning-based decision support system for gender recognition from wrist radiographs using 13,935 images collected from individuals aged between 2 and 79 years. Differences in all regions of the images, such as carpal bones, radius, ulna bones, epiphysis, cortex, and medulla, were utilized. A hybrid model was proposed for gender determination from X-ray images, in which deep metrics were combined in appropriate layers of transfer learning methods. Although gender determination from X-ray images obtained from different countries has been reported in the literature, no such study has been conducted in Turkey. It was found that gender discrimination yielded different results for males and females. Gender identification was found to be more successful in females aged between 10 and 40 years than in males. However, for age ranges of 2-10 and 40-79 years, gender discrimination was found to be more successful in males. Finally, heat maps of the regions focused on by the proposed model were obtained from the images, and it was found that the areas of focus for gender discrimination were different between males and females.
dc.identifier.doi10.36306/konjes.1294139
dc.identifier.issn2667-8055
dc.identifier.issue4
dc.identifier.trdizinid1210718
dc.identifier.urihttps://doi.org/10.36306/konjes.1294139
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1210718
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8319
dc.identifier.volume11
dc.identifier.wosWOS:001312983500017
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherKonya Teknik Univ
dc.relation.ispartofKonya Journal of Engineering Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectHand-Wrist X-ray images
dc.subjectGender identification
dc.subjectHybrid model
dc.subjectInceptionV3
dc.subjectDenseNet201
dc.titleGENDER IDENTIFICATION FROM LEFT HAND-WRIST X-RAY IMAGES WITH A HYBRID DEEP LEARNING METHOD
dc.typeArticle

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