# Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane
## 2 RA Diagnosis Methods: Modernism vs. Traditionalism
### Deep Learning Methods
According to the OESS guidelines, the joints that were scanned were the radiocarbon-intercarpal joint and radioulnar joint of the wrist, the proximal interphalangeal and the metacarpophalangeal joints. The images were then annotated by physicians with experience in ultrasound imaging. The physicians also marked the area of synovial proliferation. As the medical dataset was limited, data augmentation was used in order to create a relatively unbiased training set. The authors focus on two scenarios. The first scenario is associated with the presence of synovial proliferation and the second one is with the medical status of the patient (healthy or diseased). There were three groups of experiments performed with different inputs for the model. The variation was the different sizes of the ROI (region of interest) and the presence or not of a pre-segment mask. Furthermore, the authors insist that transfer learning could be used due to the limited quantity of medical data. It should be noticed that, for better visualization and consequently better explainability of the model outcome, heatmaps were used by means of class activation mapping. In the work described in [12], the authors present a deep learning model for automatic assignment of joint scores and overall scores for RA patients using X-ray images. They used the SvH [13] RA image scoring. Similar to other researchers, they insist that the manual approach of assigning the SvH score is expensive by means of human time and effort and is sometimes inaccurate and subjective. According to their method, there are two objectives. The first one is associated with the prediction of the narrowing and erosion scores for each joint, trying to maintain high accuracy. The second objective of their method is associated with the prediction of an overall RA score for each patient. As they used a dataset with X-ray images from hands and feet for each patient with varying dimensions, they resized the images to a standard dimension set. A data augmentation method was also used because of the small number of patients. After the augmentation, they combined the four images per patient (left/right hand/foot) so that a new larger image is created for each patient. These images were fed into a deep learning model with 13 layers of depth, achieving high accuracy. The authors of [22] try to classify RA by using deep learning models to analyze texture changes in different stages of the disease. They use the Deep Texture Encoding Network (Deep-TEN) and residual network-50 (ResNet-50) in order to predict the probability of RA. They use radiographs in order to assess the bone structural and textural changes, which indicate the progress of RA. The method of fractal analysis was used in order to determine bone texture characteristics from the radiograph images. To focus on a specific region of the images, they trained a curve-graph convolutional network (GCN). By using this model, they achieved a fully automated segmentation method of the second, third and fourth metacarpal bone regions. The segmented images were augmented. The authors based their research on the Deep-TEN model, as they insist that such a model can learn the essential features that are necessary to fit and identify the region of interest in an image regarding a specific problem. The Deep-TEN model that was used is a model specialized for texture analysis and includes a novel encoding layer, which is a point of difference in comparison to other CNN models such as ResNet. In that way, it achieves good performance in texture recognition tasks. Other authors in [14] engaged with the usage of CNN models for OESS [16] (Doppler mode in US--DUS) system assessment on ultra-sound images for diagnosis and monitoring of patients with RA. They used two state-of-the-art CNN models (VGG16, Inception V3) for two tasks. The first model was used for binary image classification as healthy or diseased (0-1), and the second model was used for OESS score assessment (0-3).