| Yazarlar (8) |
Şafak Aydın Şimşek
Türkiye |
|
Ayhan Aydın
|
|
Ferhat Say
Ondokuz Mayıs Üniversitesi, Türkiye |
|
Tolgahan Cengiz
Türkiye |
Doç. Dr. Caner ÖZCAN
Karabük Üniversitesi, Türkiye |
|
Mesut Öztürk
Türkiye |
|
Erhan Okay
Türkiye |
|
Korhan Özkan
Türkiye |
| Özet |
| This study investigates the automated detection of enchondromas, benign cartilage tumors, from x-ray images using deep learning techniques. Enchondromas pose diagnostic challenges due to their potential for malignant transformation and overlapping radiographic features with other conditions. Leveraging a data set comprising 1645 x-ray images from 1173 patients, a deep-learning model implemented with Detectron2 achieved an accuracy of 0.9899 in detecting enchondromas. The study employed rigorous validation processes and compared its findings with the existing literature, highlighting the superior performance of the deep learning approach. Results indicate the potential of machine learning in improving diagnostic accuracy and reducing healthcare costs associated with advanced imaging modalities. The study underscores the significance of early and accurate detection of enchondromas for effective patient management and suggests avenues for further research in musculoskeletal tumor detection. |
| Anahtar Kelimeler |
| deep learning | Detectron2 | enchondromas | machine learning | x-ray |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | Journal of Orthopaedic Research |
| Dergi ISSN | 0736-0266 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 07-2024 |
| Cilt No | 42 |
| Sayı | 12 |
| Sayfalar | 2826 / 2834 |
| Doi Numarası | 10.1002/jor.25938 |
| Makale Linki | http://dx.doi.org/10.1002/jor.25938 |
| Atıf Sayıları |
