## 3 Approaches
### Artificial Intelligence for Analysing Orthopaedic Trauma Radiographs
The dataset was then split in a train-validation-test with a ratio of 80:10:10. Pre-processing: Images were converted to JPEG by a trained radiologist, ensuring the most appropriate windowing was selected. Methods: The network used for this purpose was InceptionV3 [18], originally trained with the ImageNet dataset and then adapted and re-trained for the broken/unbroken classification, modifying the top-layer of the network. Hyper-parameters, such as learning rate and number of epochs, were optimized iteratively. Results: The resulting AUC value was 0.954. Conclusion: This study demonstrates that transfer learning from deep CNN pre-trained on non-medical images can easily be applied with a modest dataset size. Data augmentation was computed offline and the same augmented images were used for the whole computation. It is a good practice to do data augmentation in real time, in order to produce different augmented images for every epoch. Detection of Distal Radius Fractures Trained by a Small Set of X-Ray Images and Faster R-CNN (Region Based Convolutional Neural Network)  
This subsection focuses on the work of [PERSON] et al. [19]. Aim: Distal radius fractures are the most common fractures of the upper extremity of the human body. The authors trained Faster R-CNN [20], a machine vision neural network for object detection, to identify and locate distal radius fractures. Dataset: The initial dataset was composed of 55 images of distal radius fractures and 40 images of hands without fractures. In addition, 25 images not showing hand bones were used for the negative test set. The images were divided into 80% for training and 20% for testing. Another particularity was that images of the same hand in the dataset came in couples: anteroposterior position image (AP) and lateral position image. However, after some testing, the authors discovered that the detection neural network had better results if trained to exclude lateral images. The reason is that the two types of images are substantially different and that, in some lateral images, the fracture was hidden by other bones. Data augmentation was applied to increase the number of images to 4476 using mirroring, sharpness, brightness and contrast augmentation. Each image was labelled with bounding boxes around the fractured area. Pre-processing: Images have been converted to different resolutions from \(500\times 500\) to \(1600\times 1600\) pixels in order to assess the performance of the tool. Methods: The authors used Faster R-CNN to achieve two different tasks: classifying whether the fracture is present or not and finding the fracture's location. Faster R-CNN is an evolution of R-CNN and Fast R-CNN where region proposals are generated by CNNs rather than using selective searches. Faster R-CNN has three different phases. At first, the input images go through a CNN that extract feature maps. Secondly, a region proposal network (RPN) is used for generating region proposals, i.e., to pre-check which location contains an object without classifying the entity of the object. The output is then passed through a region of interest (ROI) pooling to perform max pooling of inputs of non-uniform sizes and obtain fixed-size feature maps. Finally, the pooled area goes through CNN and two fully connected branches for class softmax and bounding box regression, in order to detect the object class and return the bounding box of that object. The neural network used in this work was VGG16. New layers were randomly initialized by zero-mean Gaussian distributions while all other layers are initialized using the pre-trained model of ImageNet classification. Results: The classification accuracy obtained was 96% and mAP (mean average precision) score of 0.866, which is the network's precision in finding the location of the fractures.