# Classification of Shoulder X-ray Images with Deep Learning Ensemble Models
## 1 Introduction  
Having a wider and more varied range of movement than the other joints in the body, the shoulder has a flexible structure. The fractures in the shoulder may result from incidents such as dislocation of the shoulder and engaging in contact sports and motor vehicle accidents. The shoulder bone mainly consists of three different bones: the upper arm bone, named the 'humerus', the shoulder blade, named the'scapula', and the collarbone, named the 'clavicle'. Figure 1 shows the anatomic structure of the shoulder bone. The image in this figure was taken from the MURA dataset used in this study, and the markings thereon were placed by the physicians at Gazi University. As shown in Figure 1, the upper end of the humerus has a ball-like shape that connects with the scapula, called the elenoid. The types of shoulder fractures vary depending on age. While most fractures in children occur in the clavicle bone, the most common fracture in adults occur on the top part of the humerus, i.e., the proximal humerus. The types of shoulder bone fractures are divided into three categories in general: clavicle fractures, which are the most common shoulder fracture, frequently the result of a fall, scapula fractures, which rarely occur, and resulting fractures, which occur as cracks in the upper part of the arm in individuals over 65 years of age. The images from X-ray devices are primarily used for imaging of the shoulder bone for diagnosis and treatment of such fractures, while MRI or CT devices may also be used when required [1].  
Deep learning classification procedures of shoulder bone X-ray images were carried out in the study. The main contributions of this study are as follows:  
* The most suitable model for the classification of shoulder bone X-ray images as a fracture or non-fracture is determined.
* An approach that can be used in similar studies is developed via new ensemble learning models.
* The study can assist physicians who are not experts in the field in the classification stage, especially in cases of shoulder fractures, which are frequently encountered in the emergency departments of hospitals.
* The method suggested in the study contributes to the literature with two different ensemble approaches.
* With the first model proposed in the study, a performance study was conducted with transfer learning for the MURA dataset, which is widely used in X-ray studies. Thus, three models that give the best classification results have been combined into a single model, and the classification performance has been increased. The developed model can be used to classify many medical X-ray images.
* With the second model, it is determined which model finds which class best by looking at the success of finding the classes on the models in the dataset. It is ensured that the model decides the prediction of that class. Thus, regardless of the dataset studied, a similar decision system can be designed with models that find the classes in a given dataset, and a higher performance can be achieved compared to a single model.
* The proposed ensemble model approaches can be applied and generalized by performing similar preprocessing steps in other X-ray biomedical datasets. In addition, the proposed method can be easily used in studies with transfer learning.