# Classification of Shoulder X-ray Images with Deep Learning Ensemble Models
## 2 Related Works
### Classification Studies Carried Out on the Shoulder Bone  
While some of the studies mentioned under the previous title with the MURA dataset include classification of shoulder bone images, there are also studies in the literature on this type of classification exclusively. In classification carried out by [PERSON] et al. with the ResNet152 model on a total of 1891 patients, including 1376 proximal humeral fracture cases of four different types, a 96% accuracy was achieved [15]. In the CNN model proposed by [PERSON] using a total of 219 shoulder MR images, a classification was made in three different groups as normal, edematous, and Hill-Sachs lesions with a 98.43% accuracy [16]. The highest accuracy achieved in the classification carried out by [PERSON] et al. on 597 X-ray images of shoulders with implants was 80.4% in the NASNet model pre-trained with ImageNet [17]. In the classification performed by [PERSON] et al. using a total of 219 shoulder MR images grouped as 91 edematous, 49 Hill-Sachs lesions, and 79 normal, an 88% success rate was achieved with a kernel-based SVM, and 94% success was achieved with extreme learning machines [18]. A 94.74% accuracy was achieved with the proposed CapsNet model in the classification performed by [PERSON] on a total of 1006 shoulder MR images, grouped as 316 normal, 311 degenerated, and 379 torn [19]. Some other important studies on medical data classification and machine/deep learning approach in the literature are as follows: An accuracy of 93.5% was achieved in cardiac arrhythmia classification procedures with long short-term memory (LSTM) by [PERSON] and [PERSON] [20]. ResNet18 and GoogLeNet models were used by [PERSON] et al. to detect wrist bone abnormality [21]. As a result of the classification made by [PERSON] et al. for kidney disease with the multi-instance deep learning method, an accuracy of 88.6% was obtained [22]. As a result of the detection of musculoskeletal abnormalities performed by [PERSON], the highest accuracy was obtained as 81.98% with the SqueezeNet model [23]. An accuracy of 98.20% was achieved by [PERSON] and [PERSON] using spark machine learning and conv-autoencoder to detect and classify unpredictable malicious attacks [24]. Transcriptional co-activators have been identified by [PERSON] et al. as regulators of bone fracture healing [25]. The bone fracture process performed by [PERSON] et al. with machine learning and digital geometry achieved 92% classification accuracy [26]. It is observed upon examination of the recent classification studies on shoulder bone in the literature that the normal and abnormal (fracture, edematous, Hill-Sachs lesion, degenerated, or torn) images obtained from CT, MRI, or X-ray devices are classified by not only the traditional machine learning methods such as the SVM but also by deep learning-based methods such as ResNet, NasNet, and CapsNet. In this study, the classification was performed on normal and abnormal shoulder bone X-ray (fracture) images in the MURA dataset. In the performance thereof, as a practice different from the literature, built CNN-based deep learning models (ResNet, ResNeXt, DenseNet, VGG, InceptionV3, and MobileNetV2) with a varying structure and number of layers were used by transfer learning with pre-trained ImageNet by also adding SpinalNet to the classification layers. Based on the results of classification achieved by the procedures performed herein, another classification was performed with ensemble learning. The main reason for applyingtransfer learning on built CNN models, adding SpinalNet, and applying ensemble learning is to contribute to the efficiency of deep learning models proposed in the classification of shoulder bone X-ray images. Section 3 discusses the CNN-based built deep learning models, such as ResNet, DenseNet, VGG, and InceptionV3, used in classification as well as their adaptations with Spinal FC and the proposed ensemble learning models.