# Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane
## 2 RA Diagnosis Methods: Modernism vs. Traditionalism
### Deep Learning Methods
The results of the two models were compared with the results of a rheumatologist physician. They created four DUS image sets for each OESS score category. The VGG-16 model was used in order to classify the images of RA joint disease activity as healthy (DUS scores 0 and 1) or diseased (DUS scores 2 and 3). The Inception-V3 model, a more sophisticated model, has a modular architecture as it consists of several modules, the so-called inception modules. Each one of these modules extracts information from the input image following different depths, i.e., different resolutions. The information is then combined in a mixed layer. The first mixed layers contain more generic information in comparison with the latest mixed layers. Then they used the information from the mixed layer with the higher classification accuracy in order to ensemble a classification method where 10 classifiers were trained but with slightly different training parameter settings. The results show that CNN models can be used for DUS image OESS score assessment, as they achieve high accuracy. Regarding the work of [23], the authors try to use plain hand radiographs in order to create a method that automatically diagnoses rheumatoid arthritis and monitors its activity. They created an image dataset from radiographs coming from clinic files. From raw images, they kept only the right hands, maintaining the same aspect ratio from original images. They also used data augmentation to avoid overfitting and to achieve better overall performance. During the online data augmentation, random vertical and horizontal translation for several pixels and rotation of the image was used. The CNN model that was used by the authors had six groups of convolution layers. Each group consisted of one convolution layer, a batch normalization layer and a ReLU layer. The first five groups also include a max-pool layer. In the end, there is a fully connected layer and a softmax layer. According to another method described in [24], the authors propose a novel method for automatic detection of bone erosion on hand radiographs. First, their method performs a segmentation of the image in order to extract the region of interest (ROI) that contains the detailed phalanges regions. This selection is performed using the MSGVF Snakes method. Then, as the regions are selected, the method continues by using a deep neural network classifier in order to identify if there is bone erosion in these regions or not. Regarding the creation of the ROI, the proposed method removes at first the soft tissue of the radiograph image using greyscale morphological operations. After that, during the initial segmentation, the phalangeal region is extracted by estimating the joint position of each phalange from the bone region. The bone regions are located using contour tracing. At this stage, the selected regions contain noise. For this reason, the final detailed phalangeal region is created using the MSGVF Snakes method. After this final segmentation, the method can continue with the use of a DCNN so that the model can learn to distinguish if there is bone erosion in the specific region or not. It is noted that from the final 40 \(\times\) 40-pixel segmented image, only the phalange region is loaded into the DCNN. As it is generally difficult to collect medical images for model training, the transfer learning method has been used on several occasions, so it was used by the authors. They used a pre-trained model that was trained using 1000 categories of general images. The model proposed in [25], the FingerNet model, is an approach for finger detection from hand radiograph images using CNN in order to assist physicians in diagnosing diseases such as rheumatoid arthritis. Hand radiography is the simplest way for disease diagnosis and demands minimal exposure to radiation. Their method needs a little user intervention, and it consists of three different stages: the pre-processing (PP) stage, the finger extraction (FE) stage and the joint detection (JD) stage.