# Classification of Shoulder X-ray Images with Deep Learning Ensemble Models
## 2 Related Works
### Studies Conducted Using the MURA Dataset  
The MURA dataset was first introduced to the literature in a paper published in the OpenReview platform, announced in the conference on \"Medical Imaging with Deep Learning\" held in Amsterdam in 2018. Following this publication, this dataset was made publicly available for academic studies in a competition called \"Bone X-Ray Deep Learning Competition\" by the Machine Learning group of the Stanford University. Being one of the largest public radiographic image datasets, MURA contains a total of 40,561 X-ray images in png format for the following parts of the body, labeled as either normal or abnormal (fracture): elbow, finger, forearm, hand, humerus, shoulder, and wrist. In this classification study conducted by [PERSON] et al. using DenseNet-169 on this dataset, the AUC score representing the area under the overall Receiver Operator Characteristics (ROC) curve was 0.929, and the overall [PERSON]'s kappa score was 0.705 [2]. Following this first study in which this dataset was introduced to the literature, there have been various studies that have been conducted and published using all or a part of the dataset. These studies are as follows: an average precision (AP) value of 62.04% was achieved in the fracture detection procedure using the proposed deep CNN model after the fractures were marked by the physicians on the arm X-ray images in the MURA dataset by [PERSON] et al. [3]. The following classification accuracies were achieved by [PERSON] et al. using a very small part of the elbow X-ray images in the MURA dataset (56 images for the train dataset, 24 images for the test): 97% with the support vector machine (SVM), 91.6% with random forest (RF), and 91.6% with naive Bayes [4]. The classification by [PERSON] and [PERSON] with the multi-scale CNN and graph convolution network (GCN) they proposed using the entire MURA dataset achieved an overall [PERSON]'s kappa score of 0.836. [5]. The overall [PERSON]'s kappa score achieved by [PERSON] et al. in classification performed using the whole MURA dataset with the proposed capsule network architecture was 0.80115 [6]. In the classification carried out by [PERSON] et al. using the dataset containing hip bone images and the whole MURA dataset, the highest accuracy achieved was 86.53% for humerus images with the proposed adversarial policy gradient augmentation (APGA) [7]. The highest accuracy achieved in the classification performed by [PERSON] et al. using the whole MURA dataset was 79.85% with the InceptionV3 model [8]. The AUC score was 0.88 in the classification carried out by [PERSON] et al. on a Lower Extremity Radiographs Dataset (LERA) containing foot, knee, ankle, and hip data for an ImageNet and DenseNet161 model pre-trained with MURA [9]. In the classification performed by [PERSON] et al. on images of the finger, wrist, and shoulder in the MURA dataset using five different CNN-based built deep learning methods, the highest accuracy was 56.30% on the wrist data with DenseNet169 [10]. The overall accuracy achieved for MURA with the proposed iterative fusion CNN (IFCNN) method is 73.4% in classification carried out by [PERSON] et al. using the optical coherence tomography (OCT) dataset and the entire MURA dataset [11]. The overall [PERSON]'s kappa score achieved was 0.717 with the proposed deep CNN-based ensemble model in the classification carried out by [PERSON] et al. on elbow, finger, humerus, and wrist data in the MURA dataset [12]. In the type classification performed by [PERSON] et al. using the whole MURA dataset, 91.37% accuracy was achieved with the proposed deep CNN model [13]. In the classification carried out by [PERSON] and [PERSON] using the entire MURA dataset, a two-stage method was developed, and the highest accuracy achieved was in humerus images with 88.5% for the SENet154 model, while the highest accuracy for the DenseNet201 model was achieved again on humerus images with 90.94% [14].