Classification of Fracture and Normal Shoulder Bone X-Ray Images Using Ensemble and Transfer Learning With Deep Learning Models Based on Convolutional Neural Networks.

Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, due to various reasons. To diagnose these fractures the data gathered from X-radiation (X-ray), magnetic resonance imaging (MRI) or computed tomography (CT) are used. In this study, it is aimed to help physicians by classifying the shoulder images taken from X-Ray devices as fracture / non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pretrained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and 2 ensemble learning models (EL1, EL2) were developed. The pretrained models used are ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet and their versions with Spinal fully connected (Spinal FC). In EL1 and EL2 models developed using pretrained models with the highest test performance, test accuracy was 0.8455,0.8472; Cohens cappa 0.6907,0.6942; the area under the receiver operating characteristic (ROC) curve (AUC) 0.8862,0.8695 values were obtained for the fracture class. As a result of 28 different classifications in total, the highest test accuracy and Cohen cappa value were obtained in the EL2 model, and the highest AUC value was obtained in the EL1 model.