  Hospitals, especially their emergency services, receive a high number of
wrist 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 of
this study is to perform fracture detection by use of deep learning on wrist
Xray 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 based
object detection models with various backbones, 20 different fracture detection
procedures were performed on Gazi University Hospitals dataset of wrist Xray
images. To further improve these procedures, five different ensemble models
were developed and then used to reform an ensemble model to develop a unique
detection model, wrist fracture detection_combo (WFD_C). From 26 different
models for fracture detection, the highest detection result obtained was 0.8639
average precision (AP50) in the WFD-C model. Huawei Turkey R&D Center supports
this study within the scope of the ongoing cooperation project coded 071813
between Gazi University, Huawei and Medskor. Code is available at
https://github.com/fatihuysal88/wrist-d

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Medical Physics