Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Duygu Celik Ozen"'
Autor:
Şuayip Burak Duman, Ali Z. Syed, Duygu Celik Ozen, İbrahim Şevki Bayrakdar, Hassan S. Salehi, Ahmed Abdelkarim, Özer Celik, Gözde Eser, Oğuzhan Altun, Kaan Orhan
Publikováno v:
Diagnostics, Vol 12, Iss 9, p 2244 (2022)
The present study aims to validate the diagnostic performance and evaluate the reliability of an artificial intelligence system based on the convolutional neural network method for the morphological classification of sella turcica in CBCT (cone-beam
Externí odkaz:
https://doaj.org/article/7af6ef506cf0434092019423263fb57e
Autor:
Oğuzhan Altun, Duygu Çelik Özen, Şuayip Burak Duman, Numan Dedeoğlu, İbrahim Şevki Bayrakdar, Gözde Eşer, Özer Çelik, Muhammed Akif Sümbüllü, Ali Zakir Syed
Publikováno v:
BMC Oral Health, Vol 24, Iss 1, Pp 1-15 (2024)
Abstract Background Maxillofacial complex automated segmentation could alternative traditional segmentation methods to increase the effectiveness of virtual workloads. The use of DL systems in the detection of maxillary sinus and pathologies will bot
Externí odkaz:
https://doaj.org/article/63d90640b3f648b4acd19e07a7a1f500
Autor:
Mustafa Temiz, Suayip Burak Duman, Ahmed Z. Abdelkarim, Ibrahim Sevki Bayrakdar, Ali Z. Syed, Gozde Eser, Duygu Celik Ozen, Hatice Tugce Gedik, Mehmet Ugurlu, Rohan Jagtap
Publikováno v:
Science Progress. 106:003685042311571
Objective: This study aimed to examine the morphological characteristics of the nasopharynx in unilateral Cleft lip/palate (CL/P) children and non-cleft children using cone beam computed tomography (CBCT). Methods: A retrospective study consisted of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4add05c07775c0d72420dd4be3afc02
https://doi.org/10.1177/00368504231157146
https://doi.org/10.1177/00368504231157146
Autor:
Pelin Senem Ozsunkar, Duygu Çelİk Özen, Ahmed Z Abdelkarim, Sacide Duman, Mehmet Uğurlu, Mehmet Rıdvan Demİr, Batuhan Kuleli, Özer Çelİk, Busra Seda Imamoglu, Ibrahim Sevki Bayrakdar, Suayip Burak Duman
Publikováno v:
BMC Oral Health, Vol 24, Iss 1, Pp 1-11 (2024)
Abstract Background Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based Y
Externí odkaz:
https://doaj.org/article/d81690ea29474ab9a5a5c20b9e4cbb55