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Hospitals, especially their emergency services, receive a high number ofwrist fracture cases. For correct diagnosis and proper treatment of these,images obtained from various medical equipment must be viewed by physicians,along with the patients medical records and physical examination. The aim ofthis study is to perform fracture detection by use of deep learning on wristXray images to support physicians in the diagnosis of these fractures,particularly in the emergency services. Using SABL, RegNet, RetinaNet, PAA,Libra R_CNN, FSAF, Faster R_CNN, Dynamic R_CNN and DCN deep learning basedobject detection models with various backbones, 20 different fracture detectionprocedures were performed on Gazi University Hospitals dataset of wrist Xrayimages. To further improve these procedures, five different ensemble modelswere developed and then used to reform an ensemble model to develop a uniquedetection model, wrist fracture detection_combo (WFD_C). From 26 differentmodels for fracture detection, the highest detection result obtained was 0.8639average precision (AP50) in the WFD-C model. Huawei Turkey R&D Center supportsthis study within the scope of the ongoing cooperation project coded 071813between Gazi University, Huawei and Medskor. Code is available athttps://github.com/fatihuysal88/wrist-d

Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models