# Dementia Prediction Support Model Using Regression Analysis and Image Style Transfer
## 4 Results and Performance Evaluation  
The data used in this study are the brain images and patient information data according to the dementia stages provided by OASIS. In the patient information data, preprocessing is made; e.g., the removal of unnecessary variables, etc., and, with the clinical dementia rating as the dependent variable and other variables as independent variables, regression analysis is conducted. The brain condition related to MMSE is recollected and extracted accordingly, and, unless the size of the collected image and that of the recollected image are the same, there may be an error in the CNN operation. Thus, all images are adjusted to the same size to transfer the style. In addition, the weighted value of the content image in the model is that of the clinical dementia stage, and 0 means non-demented; 0.5, very mild; 1, mild; 2, moderate. To prevent the transfer of information about unnecessary attributes, the background of all images is removed before proceeding. The development environment of the research is hardware AMD Ryzen 9 5900X 12-Core Processor 3.70 GHz, 64 GB RAM, GeForce RTX 3090(NVIDIA). In addition, as a software environment, Python 3.7 and Anaconda 4.5.11 are employed. The performance evaluation makes a comparison  
Figure 6: Result of image style transfer. with similarity to the image transferred in style according to the dementia rating. Figure 7 shows the data processing process for performance evaluation. The data processing process for performance evaluation in Figure 7 generates MRI data that cut the brain on the axial plane. Accordingly, unnecessary backgrounds are removed, leaving a part of the brain where each character can be identified. Finally, the remaining image data unify T1 and T2. In the case of T1, the water component appears in black, and the fat component appears in white. Alternatively, in the case of T2, contrary to T1, the water component appears white, and the fat component appears black. Therefore, in order to unify this, in the case of T2, black and white are reversed so that the same characteristics as in T1 are displayed. Further, the size of the image is created to be the same as 110 \(\times\) 150. Accordingly, images are processed under the same conditions, the results are extracted through the proposed model, and a performance evaluation is performed. The evaluation method for style transfer includes qualitative evaluation and quantitative evaluation. The qualitative evaluation depends on the subjective aesthetic judgment of the observer. Conversely, a quantitative evaluation can conduct an objective evaluation according to the evaluation index, including time complexity or data loss change, etc. It is complex to define the concept of style, and, since the definition of the standard is  
Figure 7: Data processing process for performance evaluation. subjective, it is difficult to evaluate the result [25]. Thus, in this study, the evaluation of the proposed model is conducted using a quantitative evaluation method. First, the similarity is compared between the styled image and the actual image of the brain of the patients with dementia. As an evaluation index for a comparison of image similarity, Structural Similarity (\(SSIM\)) is used. SSIM is a method for evaluating image quality, which evaluates the original image. This has a value between zero and one, and the closer to one, the higher the similarity to the natural image. It is possible to evaluate image similarities by considering Luminance, Contrast, and Structure [26; 27]. Equation (1) shows the \(SSIM\). \(X\) and \(Y\) respectively mean the image; \(L\) is Luminance, \(C\) is Contrast, and \(S\) is Structure. \[SSIM(X,Y)=I(X,Y)\times C(X,Y)\times S(X,Y) \tag{1}\]  
Through Equation (1), the results of the transfer of dementia stages and the styles of the factors affecting dementia are compared and extracted. In addition, the parts different from the dementia brain are marked to compare the \(SSIM\) value.