This paper presents a deep learning-based model for automatic shoulder fracture detection using EfficientNet-B0. The model was trained on the XR_SHOULDER subset of the MURA dataset. The achieved accuracy of about 80% confirms the effectiveness of the proposed approach.
| Mualliflar | Uteuliev, Nietbay Uteulievich, Djaykov, Gafur Muratbaevich, Artikbaev, Javlonbek Ziyatbaevichi, Sagidullaev, Nursultan Ibadullaevich, Утеулиев, Ниетбай Утеулиевич, Джайков, Гафур Муратбаевич, Артыкбаев, Жавлонбек Зиятбаевич, Сагидуллаев, Нурсултан Ибадуллаевич, Uteuliev, Nietbay Uteulievich, Djaykov, Gafur Muratbaevich, Artikbaev, Javlonbek Ziyatbay o‘g‘li, Sagidullaev, Nursultan Ibadullaevich |
|---|---|
| Jurnal | Ilim ha’m ja’miyet |
| Nashr sanasi | 2026-05-16 |
| Son | 3-1 |
| Betlar | 30-33 |
| Til | Rus |
чуқур ўрганиш, EfficientNet, рентген тасвирлари, синишлар, MURA, классификация, глубокое обучение, EfficientNet, рентгенография, переломы, MURA, классификация, deep learning, EfficientNet, radiography, fractures, MURA, classification
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