## 3 Experiment Results
### Experiment Result with All Test Data  
Table 2 presents experimental results using all test data with a ratio of seven normal and three ICH. The experimental results using the loss function based on Equation (2) are shown in Table 2 as \(L\) w/\(p\) value 1 and \(L\) w/\(p\) value 10. Within the group using the \(l\) loss function, EfficientNet-B5+B scored the highest performance across all metrics, showing the overall highest score among all experimental results in Table 2. In the same model, the cosine similarity using ClinicalBERT was 0.78, confirming that the similarity between the generated sentences and the actual report was high. For the ResNet-50 model, except for the Top-k Sampling generation strategy, the scores were higher when using \(L\) w/\(p\) value 1 and \(L\) w/\(p\) value 10 compared to using \(l\), and they showed the highest scores among the models using the same loss function group. This indicates that the penalty-applied loss functions had a positive impact. The reported METEOR and ROUGE-L scores from [PERSON] et al., which placed first in the imageCLEFmedical Caption Task 2022 for single medical image captioning, were 0.09 and 0.14, respectively, while the reported BLEU score from [PERSON] el al., which ranked third in the same competition, was 0.31 [8,9]. These results show that the proposed method in this study demonstrates a certain level of usability utilizing 3D-CNN models to consider the spatial information, even for a more challenging task, which should consider the spatial information compared to a single medical image captioning task.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Loss Function** & **Models** & **BLEU \({}^{*}\)** & **METEOR** & **ROUGE-L** & **Cosine Similarity** \\ \hline \multirow{8}{*}{\(l\)} & ResNet-50 + G & 0.26 & 0.19 & 0.51 & 0.75 \\  & ResNet-50 + B & 0.31 & 0.21 & 0.54 & 0.77 \\  & ResNet-50 + T & 0.08 & 0.06 & 0.13 & 0.58 \\  & EfficientNet-B5 + G & 0.28 & 0.20 & 0.52 & 0.76 \\  & EfficientNet-B5 + B & **0.35** & **0.23** & **0.56** & **0.78** \\  & EfficientNet-B5 + T & 0.09 & 0.08 & 0.17 & 0.62 \\  & DenseNet-201 + G & 0.28 & 0.20 & 0.53 & 0.76 \\  & DenseNet-201 + B & 0.23 & 0.16 & 0.44 & 0.73 \\  & DenseNet-201 + T & 0.07 & 0.06 & 0.12 & 0.57 \\  & ConvNeXt-S + G & 0.26 & 0.19 & 0.51 & 0.75 \\  & ConvNeXt-S + B & 0.25 & 0.19 & 0.51 & 0.75 \\  & ConvNeXt-S + T & 0.06 & 0.05 & 0.12 & 0.57 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental result with all test data. G denotes Greedy Search, B denotes Beam Search, and T denotes Top-k Sampling. Highest score for each metric within the loss function is highlighted with bold text.  
However, as can be seen in Figure 4a,b, there are significant differences in the report that makes up the radiology reports of the images within the test dataset. Figure 4a shows a radiology report of a normal CT scan, which is mainly composed of similar sentences and includes many sentences that are the same or similar to None-ICH texts. In contrast, Figure 4b shows a radiology report of an ICH CT scan, which contains various information. The experimental results presented in Table 2 utilized test data where the proportion of normal CT scans was around 70%, similar to the training data. This suggests that the model's performance could be inaccurately evaluated if it primarily generates sentences similar to those found in Figure 4a or None-ICH texts. Furthermore, since one of the goals of this study is to generate reports for the patient's CT scans, it is essential to evaluate the performance of the model, specifically for the ICH CT scans. Therefore, we conducted experiments using only the test data of ICH CT scans.