# Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions
## 4 Results
### Baseline and TL  
To set the baseline performance by a model learned on our training dataset, we tested a vanilla ResNet-152 network trained from scratch. For performance evaluation, we used model accuracy (i.e., \(\frac{\text{correct predictions}}{\text{all predictions}}\)) for the test dataset. Figure 6 shows the accuracy of the vanilla ResNet-152 and TL models with respect to various sizes of labeled datasets used in training. We used the performance of vanilla ResNet-152 as a _baseline_ for comparison with other sample efficient models. As expected, the larger the dataset, the better the model accuracy. For example, the baseline model learned on a small dataset of 20 samples per class (for short, dataset20) yielded 44.5%, and the model learned on a large dataset of 1700 samples per class (dataset1700) yielded 73.3%. In the following, we use term dataset\(K\) where \(K\) denotes the number of samples per class. As there is no publicly available burn image dataset, it is rarely possible to directly compare model accuracy with that of other research studies on burn severity assessment. While the intermediate partial thickness type is difficult for clinicians to correctly evaluate [50] and affects the performance of burn severity assessment models, several studies [9, 18, 51] have presented such models learned on datasets without much consideration of the intermediate partial thickness type. When we built and tested a model after removing the samples of the intermediate partial thickness type from our dataset, we observed that our baseline accuracy (i.e., 4-class classification) was no lower than 88%, which was much higher than the baseline with the intermediate partial thickness type (i.e., 5-class classification). There have been only a small number of studies processing burn image datasets with the intermediate partial thickness type and training models on those datasets. In [17], a dataset with different burn types, including the intermediate partial thickness type, was used to train deep CNN models for time-independent burn type inference. The models achieved quite high accuracy (e.g., average 73.8\(\sim\)81.7% in Table 3 in [17]), which can be considered slightly higher than our baseline performance. It should be noted that, as we  
Figure 6: Baseline and TL performance: the graph shows the model accuracy (on the Y-axis) of the vanilla ResNet-152 (baseline) and TL models learned on labeled datasets in various sizes, where the number of training samples per class (burn type) is denoted on the X-axis. For example, 20 and 40 on the X-axis represent models learned on the datasets with 20 and 40 samples per burn type (e.g., superficial partial, intermediate partial, deep partial, full thickness, and normal), respectively. used our own burn image dataset and sought to adopt sample-efficient learning techniques under data-limited conditions with the dataset, in our experiments, we did not consider it important to compare model performance across different datasets. We also evaluated TL models that exploited a pretrained ResNet-152 network built on an ImageNet dataset. The TL models showed better accuracy than the respective baseline models in most cases, e.g., 13.1% higher in dataset20 and 9.2% higher in dataset40. However, this gap decreased for larger datasets. It is hypothesized that features relevant to burn types were not fully extracted and represented by the pretrained model due to domain differences between the burn images and diverse images in the ImageNet dataset. The last layer parameter updates in TL might be sufficient to improve the accuracy of low-performance models learned on small datasets. However, the margin of improvement can be less significant for relatively large datasets. In dataset510, the gain of TL from the baseline was small, increasing from 67.1% to 69.5%, and in dataset1700, it barely increased and remained around 73%. Overall, these results indicate the benefits of TL, particularly for small datasets; they also clarify the limited capability of TL across different domains.