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Fractures occur in the shoulder area, which has a wider range of motion thanother joints in the body, for various reasons. To diagnose these fractures,data gathered from Xradiation (X-ray), magnetic resonance imaging (MRI), orcomputed tomography (CT) are used. This study aims to help physicians byclassifying shoulder images taken from X-ray devices as fracture / non-fracturewith artificial intelligence. For this purpose, the performances of 26 deeplearning-based pretrained models in the detection of shoulder fractures wereevaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemblelearning models (EL1 and EL2) were developed. The pretrained models used areResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fullyconnected (Spinal FC) versions. In the EL1 and EL2 models developed usingpretrained models with the best performance, test accuracy was 0.8455,0.8472,Cohens kappa was 0.6907, 0.6942 and the area that was related with fractureclass under the receiver operating characteristic (ROC) curve (AUC) was0.8862,0.8695. As a result of 28 different classifications in total, thehighest test accuracy and Cohens kappa values were obtained in the EL2 model,and the highest AUC value was obtained in the EL1 model.

Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models