# Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions
## 4 Results
### GAN-Based Data Augmentation  
We tested several data augmentation techniques including GAN-based ones. Figure 9 shows the performance of baseline models on augmented datasets. For example, 80 on the X-axis corresponds to the models for which the dataset (originally, dataset80) was doubled in size by the conventional transformation methods described in Section 3.3. The models learned on augmented datasets rarely showed better accuracy, compared to their respective baseline models, except for RBC (RandomBrightnessContrast). While it might be difficult for these conventional transformation methods to generate new images which contain adequate relevant features about different burn types, RBC tends to enhance the color characteristics of burn injury parts. In Figure 10, we represent the model accuracy acquired by GAN-based data augmentation methods, where two GAN models are tested to augment datasets such as CycleGAN  
Figure 8: FL performance: the graphs show the model accuracy (on the Y-axis) achieved according to different group sizes in (**a**) and different epochs per round in (**b**), as federation proceeds in rounds (on the X-axis). In (**a**), we tested different FL group sizes from 10 to 30, and in (**b**), we tested different numbers of epochs per round from 10 to 30, where both default values were set to 20. We used the same model as the baseline (the vanilla ResNet-152) for most cases, except for the case of 20(TL) in dotted red lines that used the pretrained ResNet-152 with a group of 20 clients with 20 epochs per round. Figure 9: Performance of data augmentation: the graphs show the model accuracy (on the Y-axis) achieved through data augmentation by conventional techniques listed in Section 3.3 with respect to the sizes of training datasets (on the X-axis). Each dataset is augmented to double the size. and StyleGAN. In this experiment, when training the GAN models, labeled burn images were used restrictively. For example, with 80 on the X-axis (dataset80), the respective CycleGAN and StyleGAN were learned on dataset80, as we focused on data-limited conditions. In this way, we ensured our experimental conditions of limited training data such that each GAN model was not exposed to more labeled burn images (the images of different burn types except for normal skin images) than the corresponding burn severity models. We observed performance gains by both GAN-based methods, compared to the baseline, with a larger performance gain observed for CycleGAN than for StyleGAN. We also observed that the gain increased for dataset120 and dataset170 more than for dataset80. This was because the quality of synthetic images generated by the GAN models was dependent on the quantity of samples. In Figure 11, we represent the overall performance comparison among several sample-efficient deep learning techniques. While they can be adopted and optimized differently according to various conditions of limited datasets, we compared their accuracy on dataset170 as a representative case. Overall, the SSL models with pretraining on unlabeled images and full layer fine-tuning (SSL-burn-all) showed better accuracy than the other models. For example, SSL-burn-all on dataset170 achieved 70% while the baseline and TL models achieved 61.6% and 66.7% on the same dataset, respectively. We also applied the CycleGAN-based data augmentation on SSL-burn models (SSL-burn-all+CycleGAN), combining two sample-efficient methods; each demonstrated advantages for improve model accuracy in Figures 7 and 10, respectively. However, despite our expectation, we observed that this rarely improved the model accuracy compared to the respective SSL-burn model. Synthetic images by GAN can improve the model accuracy of low-performance models (e.g., the baseline model), but they rarely do much for non-low-performance models. To generate high-quality synthetic images by GAN techniques, which can improve the model performance for burn image datasets, more research on GAN model optimization is required.