Human gender prediction based on deep transfer learning from panoramic dental radiograph images

Basit Öğe Kaydı
dc.authorid0000-0003-4094-9598en_US
dc.contributor.authorAtaş, İsa
dc.date.accessioned2023-08-10T07:30:21Z
dc.date.available2023-08-10T07:30:21Z
dc.date.issued2022en_US
dc.departmentDicle Üniversitesi, Diyarbakır Teknik Bilimler Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractPanoramic Dental Radiography (PDR) image processing is one of the most extensively used manual methods for gender determination in forensic medicine. With the assistance of the PDR images, a person's biological gender determination can be performed through analyzing skeletal structures expressing sexual dimorphism. Manual approaches require a wide range of mandibular parameter measurements in metric units. Besides being timeconsuming, these methods also necessitate the employment of experienced professionals. In this context, deep learning models are widely utilized in the auto-analysis of radiological images nowadays, owing to their high processing speed, accuracy, and stability. In our study, a data set consisting of 24,000 dental panoramic images was prepared for binary classification, and the transfer learning method was used to accelerate the training and increase the performance of our proposed DenseNet121 deep learning model. With the transfer learning method, instead of starting the learning process from scratch, the existing patterns learned beforehand were used. Extensive comparisons were made using deep transfer learning (DTL) models VGG16, ResNet50, and EfficientNetB6 to assess the classification performance of the proposed model in PDR images. According to the findings of the comparative analysis, the proposed model outperformed the other approaches by achieving a success rate of 97.25% in gender classification.en_US
dc.identifier.citationAtaş, İ. (2022). Human gender prediction based on deep transfer learning from panoramic dental radiograph images. Traitement du Signal, 39(5), 1585-1595.en_US
dc.identifier.doi10.18280/ts.390515
dc.identifier.endpage1595en_US
dc.identifier.issn0765-0019
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85150244034
dc.identifier.scopusqualityQ3
dc.identifier.startpage1585en_US
dc.identifier.urihttps://www.iieta.org/journals/ts/paper/10.18280/ts.390515
dc.identifier.urihttps://hdl.handle.net/11468/12462
dc.identifier.volume39en_US
dc.identifier.wosWOS:000907630800007
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAtaş, İsa
dc.language.isoenen_US
dc.publisherInternational Information and Engineering Technology Associationen_US
dc.relation.ispartofTraitement du Signal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDenseNet121en_US
dc.subjectDeep convolutional neural networken_US
dc.subjectDeep transfer learningen_US
dc.subjectGender predictionen_US
dc.subjectPanoramic dental radiographen_US
dc.titleHuman gender prediction based on deep transfer learning from panoramic dental radiograph imagesen_US
dc.titleHuman gender prediction based on deep transfer learning from panoramic dental radiograph images
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Human Gender Prediction Based on Deep Transfer Learning from Panoramic Dental Radiograph Images.pdf
Boyut:
1.42 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Makale Dosyası
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Diyarbakır Teknik Bilimler Meslek Yüksekokulu Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu