## 4 Discussion and Conclusions
One idea to tackle this could be to assign different weights to different classes when computing the cost function--classes with few images will be associated with higher weights. In addition, technologies such as generative adversarial networks [32] might be used to generate \"fake\" fractured bones, but this would probably be unfeasible, as the fractures might appear unrealistic. Concerning the pre-processing phase, the dataset should be cleaned from images containing prosthetics or other evident defects, and we recommend using no more than one image per person to decrease over-performance by the inclusion of very similar images of the same patient. It is also demonstrated that selecting the fractured area and feeding the neural network with cropped images instead of the full image improves the network performance [24; 28; 30]. For this reason, a fully automated tool to select the fractured regions should be designed for the pre-processing phase. In addition, it is suggested that a lossless format is used, such as PNG or TIFF, and that images are resized to different sizes in order to see which one works best [19]. Unfortunately, this is not feasible when using an existing network with transfer learning, because the input images must have a fixed size. Transfer learning allows the use of a network pre-trained on a different dataset for your own dataset. The most-used network architectures were VGG, ResNet, DenseNet and Inception, pre-trained with an ImageNet dataset. As demonstrated in different papers, pre-training the network using a larger dataset of X-ray bone images may improve the performance [10; 26]. For example, the MURA dataset [26] is one of the biggest bone datasets freely available. For this reason, we recommend trying different networks pre-trained with the MURA dataset and testing which one works best for the specific problem at hand. Moreover, the hyperparameters, which must be set correctly after some trials, are the learning rate (with transfer learning it is better to use a low learning rate in order not to change the pre-trained weights too much), the learning rate decay and the batch size. Cross-validation should be used to demonstrate that the network correctly generalizes the dataset features [13; 30]. Another improvement could be introduced by removing surplus imaging data [13]. For example, if a network has to classify between the no-fracture class and three different types of fractures (A, B and C) it works \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Paper N\({}^{\circ}\)** & **AUC** & **Accuracy** & **Dataset Size** & **Sensitivity** & **Specificity** & **Labelling** \\ \hline [PERSON] et al. [28] & B: 0.98 & B: 94\% & TR: 942 & NA & NA & N: 3 \\  & M: 0.95 & M: 91\% & TE: 269 & & & C: 2 \\  & & & TR: 636 & & & \\ [PERSON] et al. [30] & NA & B: 97\% & TE: 159 & B: 94\% & B: 96\% & NA\({}^{*}\) \\ \hline \multicolumn{8}{l}{\({}^{*}\) Label outcomes were assigned according to the diagnosis in the trauma registry and followed by a check.} \\ \end{tabular}
\end{table} 
Table 1: _Cont._at its best if trained to exclude no-fracture images. Following these results and the AO foundation classification, one idea could be to apply a hierarchical approach. To be clearer, an initial network that classifies between fracture and no-fracture and a subsequent one that takes the images predicted to be fracture and classifies them into three types (A, B and C). Finally, class activation mapping or similar technologies should be used to see where the network is focusing [21; 26; 28]. A clear example is found in [30], where the author understood, with the help of CAM, that the network was learning the wrong features. Last but not least, the specialists play a fundamental role: the dataset must be correctly labelled, and different years of experience are needed to properly classify the types of fractures following the AO classification, especially the subgroups.