# Dementia Prediction Support Model Using Regression Analysis and Image Style Transfer
## 3 Dementia Prediction Support Model Using Regression Analysis and Image Style Transfer
### Dementia Prediction Support Model Using Image Style Transfer  
NST (Neural Style Transfer) is the process for rendering images of various style contents, using CNN. It includes Image-Optimization-Based Online Neural Methods (IOB-NST) that transfer styles by repeatedly optimizing the image and Model-Optimization-Based Offline Neural Methods (MOB-NST), which optimizes the generated model offline and generates the image styled with a single forward pass. However, firstly, it costs a lot to calculate the procedure of repeatedly optimizing the image, and there is an efficiency issue. In addition, generally, NST captures only the characteristic correlation per pixel and does not limit the space layout. Thus, there is a disadvantage in that the result is visually unstable (less plausible) for a realistic style. To resolve this, [PERSON] et al. (2020) modeled the style with a non-parametric method and applied a new style loss function, which included patch-based MRF. In contrast, secondly, the method of optimizing the model offline can resolve problems with speed and calculation cost by reconstructing the styled result. The dementia diagnosis support model proposed in this study is in an optimization-based style transfer method. However, to resolve the problem of the unstable results from the optimization-based style transfer method, the clinical dementia rating is taken as the weighted value of the content image.  
The model transforms the normal brain into a brain with a disease by transferring information about the attributes of the image. For example, the attributes of an alcohol-addicted brain, a brain with cerebral hemorrhage, and a traumatic brain injury, recollected in the images of the normal brain, are transferred in order to transform the brain into the brain of [PERSON]'s disease. Accordingly, it is possible to monitor the factors affecting dementia and the transformation process, as well as to predict the disease.  
Different positions of the object and different numbers of layers in the convolution operation represent very different visual results. Thus, the selection of contents and style layers are important factors in the style transfer process. Image attributes include image color and texture, etc. The proposed dementia prediction support model is designed based on the VGG network. In VGG, the size of an input image is fixed to 110 \(\times\) 150 and pre-processing removes the average value of RGB. The convolution layer uses a 3 \(\times\) 3 filter to consider the pixel values at the top, bottom, left, and right. The filter's stride is one, and padding is applied for operation. In the pooling layer, maximum pooling is applied next to the convolution layer, which consists of a total of five. Figure 5 shows the diagram of the proposed dementia prediction support model.  
Figure 5: Diagram of the proposed dementia prediction support model.  
Style transfer uses the features of the VGG19 network consisting of convolution, pooling layers, and an FC layer. The model in Figure 5 is divided into the extractor and the classifier of features. The input image is divided into a content image and a style image. From each convolution layer and each pooling layer, the features of the content image and style image are extracted, respectively. In addition, the FC layer and SoftMax layer features are calculated, classified, and predicted to transfer the style. Figure 6 shows the result of the image style transfer. The content image is the normal human brain image. The style image is the recollected brain image. The resulting image is the result extracted by transferring the attributes of the style image to the content image.  
As a result of the transfer of image style in Figure 6, the style of dementia factors in normal brain conditions is applied; therefore, there are changes in brain volume. Thus, dementia factors have serious impacts on dementia as well as cognitive disorder; it is necessary to be careful when managing and preventing it.