# Predicting Post-Liposuction Body Shape Using RGB Image-to-Image Translation
## 4 Results  
**(Data Augmentation)** To determine whether the CcGAN correctly generated body shape images according to the input BMI value, we used the ResNet34-based BMI predictor, as mentioned in Section 3. The CcGAN was trained over a total of 20,000 iterations, and the generator model was saved every 1000 iterations. For training the CcGAN, we used the Adam optimizer with a learning rate of 0.0002, beta values of (0.5, 0.999), and a batch size of 32, following standard GAN training practices [12]. Continuous conditional labels (BMI values) were embedded into a 128-dimensional vector to ensure stable training. The model was trained for approximately 286 epochs, equivalent to 20,000 iterations, given the dataset size and batch size. The training was conducted on an NVIDIA RTX 2060 GPU. The images generated by each model were evaluated for accuracy by the BMI predictor. We calculated MSELoss between the predicted BMI and the actual BMI. As MSELoss approaches 0, the generated image means that the image corresponding to the target BMI Label is generated. Figure 5 presents a graph showing the MSELoss calculated every 1000 iterations, along with some of the images generated. During the initial iterations, the images were severely blurred, and the body type did not vary with BMI. The 14,000 th-iteration generator model was deemed optimal as it achieved the smallest MSELoss (0.0119) and could generate appropriate body shapes for different BMI values. The 19,000 th-iteration model suffered mode collapse [40] and could generate only similar images or visually abnormal images. Therefore, the 14,000 th-iteration generator model was used for data augmentation. Equation (7) presents the linear regression equation for calculating the BMI after liposuction based on the BMI before liposuction. Table 2 summarizes the metrics used to evaluate the effectiveness of the linear regression process. R\({}^{2}\) represents the explanatory power of linear regression; the closer its value is to 1, the more accurate the prediction. The R\({}^{2}\) value was determined to be 0.912 in this study, which underscores the accuracy of our predictive model. Post-liposuction BMI = 0.8706 \(\times\) Pre-liposuction BMI + 1.7281. With a fixed noise z, the CcGAN generator created images of individuals before and after liposuction. Figure 6 shows the images generated considering a total of 4 noise z. When the BMI before liposuction was low, no significant difference could be observed between the images before and after liposuction. This occurs because the BMI values before and after liposuction share a linear relationship, whereby small BMI values are not significantly altered after liposuction. Accordingly, the larger the BMI before liposuction, the more pronounced the difference in body shape between the images before and after liposuction. The pre-liposuction and post-liposuction images in Figure 7 can be confirmed as depicting the same person for every z value. \begin{table}
\begin{tabular}{c c} \hline
**Metric** & **Value** \\ \hline R\({}^{2}\) & 0.912 \\ F-statistics & 3063 \\ Prob (F-statistics) & 1.76 \(\times\) 10\({}^{-106}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of linear regression predicting post-liposuction BMI from pre-liposuction BMI. This table presents the results of a linear regression model used to estimate BMI after liposuction based on BMI before surgery. The model achieved a high R\({}^{2}\) value of 0.912, indicating strong predictive power. The F-statistics and corresponding \(p\)-value (1.76 \(\times\) 10\({}^{-106}\)) confirm the statistical significance of the model. These results validate the use of linear regression for estimating post-surgical BMI, which was then used to condition synthetic image generation in the CcGAN framework. Figure 6: BMI Predictor Loss (mean squared error) and Corresponding Image Samples at Key Iterations during CcGAN training. The graph shows the change in MSE loss values predicted by the ResNet34-based BMI predictor for images generated by CcGAN at every 1000 iterations.