## 3 Approaches
### MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs  
This subsection focuses on the work of [PERSON] et al. [26]. Aim: The aim of this work was to train a baseline model that is able to classify whether a radiographic study is normal or abnormal: a study interpreted as normal can eliminate the need for patients to undergo further diagnostic procedures or interventions. The authors introduced the MURA dataset, one of the largest public radiographic image datasets. Dataset: MURA dataset contains 14,863 musculoskeletal studies of the upper extremity. Each study contains one or more images taken from different views, with a total of 40,561 images, and was manually labelled by radiologists as normal or abnormal. The studies are divided in 9045 normal and 5818 abnormal for seven different extremities, including the shoulder, humerus, elbow, forearm, wrist, hand, and finger. The dataset was split into training (36,808 images), validation (3197 images), and test (556 images) sets. Data augmentation was also applied with lateral inversions and rotations (maximum 30\({}^{\circ}\)). Pre-processing: Each image was normalized to have the same mean and standard deviation and scaled to 320 \(\times\) 320 pixels. Methods: The model used is a 169-layer convolutional neural network based on the architecture of DenseNet that takes as input one or more views for the same study. The output is a binary prediction, where a bone is abnormal if the output is greater than 0.5. For this reason, the final fully connected layer was replaced with a single output layer and a sigmoid non-linearity. The weights were initialized after training with the ImageNet dataset. Finally, the authors implemented CAM to visualize where the network focused during classification. Results: The AUC of the model was 0.929. Sensitivity and specificity were 81% and 89% respectively. Plus, the authors chose three radiologists to create a gold standard, defined as the majority vote of the labels of the radiologists and then compared radiologists and model performances with the [PERSON]'s kappa statistic (K) [27], which expresses the agreement of each radiologist/model with the gold standard. On studies of fractures of fingers, the model's performance was 0.389, which was comparable to the best radiologist performance of 0.410. Similarly, on the wrist, the model's performance was 0.931, which was the same for the best radiologist. On all other skeletal sites and overall, the model's performance was lower than the best radiologist's performance. The AUC of the model was 0.929. Conclusion: The dataset used by the authors is freely available, which implies that may be used by other researchers in order to improve the performance of the model. The dataset could also be used to pre-train a model for bone classification that can rely only on a small dataset. Towards an Interactive and Interpretable (computer-aided diagnosis) CAD System to Support Proximal Femur Fracture Classification  
This subsection focuses on the work of [PERSON] et al. [28]. Aim: This work proposed a fully automatic CAD tool that is able to identify, localize and finally classify proximal femur fractures on X-rays images according to the AO classification. Following AO classification, proximal femur fractures were divided into three main groups: A, B and C, depending on the area that is involved. Each of these classes are subsequently divided into sub-groups. Dataset: The dataset is composed of a total of 1347 X-ray images. For the two-class problem, 780 fracture images and 567 normal images were considered. The same dataset was used for the three-class problem, considering 327 images of type A fractures, 453 of type B fractures and 567 normal X-rays. Three clinical experts participated in the evaluation: one fifth-year resident trauma surgeon, one trauma surgery attendant and one senior radiologist. The dataset was split into three parts with the ratio 70:10:20 to build, respectively the training, validation and the test set.