## 3 Experiment Results
### Experiment Result with the Test Data Consisted of ICH CT Scan  
Table 3 presents the experimental results using test data composed solely of ICH CT scans. In the imageCLEFmedical Caption Task 2022, the highest reported METEOR score was 0.09, while the ROUGE-L score was 0.20 [7]. Some of the results in Table 3 \begin{table}
\begin{tabular}{c c c c c c} \hline
**Loss Function** & **Models** & **BLEU \({}^{*}\)** & **METEOR** & **ROUGE-L** & **Cosine Similarity** \\ \hline \multirow{8}{*}{\(L\) w/\(p\) value 1} & ResNet-50 + G & 0.29 & 0.19 & 0.51 & 0.76 \\  & ResNet-50 + B & **0.33** & **0.22** & **0.54** & **0.77** \\  & ResNet-50 + T & 0.07 & 0.06 & 0.12 & 0.58 \\  & EfficientNet-B5 + G & 0.24 & 0.17 & 0.44 & 0.73 \\  & EfficientNet-B5 + B & 0.21 & 0.15 & 0.34 & 0.68 \\  & EfficientNet-B5 + T & 0.09 & 0.08 & 0.15 & 0.60 \\  & DenseNet-201 + G & 0.27 & 0.20 & 0.52 & 0.76 \\  & DenseNet-201 + B & 0.19 & 0.12 & 0.33 & 0.66 \\  & DenseNet-201 + T & 0.07 & 0.06 & 0.13 & 0.59 \\  & ConvNet-S + G & 0.26 & 0.19 & 0.51 & 0.75 \\  & ConvNet-S + B & 0.25 & 0.19 & 0.51 & 0.75 \\  & ConvNet-S + T & 0.06 & 0.05 & 0.11 & 0.56 \\ \hline \multirow{8}{*}{\(L\) w/\(p\) value 10} & ResNet-50 + G & 0.28 & 0.2 & 0.52 & 0.76 \\  & ResNet-50 + B & **0.31** & **0.22** & **0.54** & **0.77** \\  & ResNet-50 + T & 0.08 & 0.07 & 0.14 & 0.59 \\  & EfficientNet-B5 + G & 0.27 & 0.17 & 0.45 & 0.73 \\  & EfficientNet-B5 + B & 0.20 & 0.15 & 0.34 & 0.67 \\  & EfficientNet-B5 + T & 0.11 & 0.09 & 0.18 & 0.63 \\  & DenseNet-201 + G & 0.28 & 0.20 & 0.53 & **0.77** \\  & DenseNet-201 + B & 0.24 & 0.16 & 0.44 & 0.75 \\  & DenseNet-201 + T & 0.09 & 0.07 & 0.16 & 0.6 \\  & ConvNet-S + G & 0.06 & 0.1 & 0.16 & 0.56 \\  & ConvNet-S + B & 0.06 & 0.11 & 0.14 & 0.53 \\  & ConvNet-S + T & 0.03 & 0.01 & 0.11 & 0.56 \\ \hline \end{tabular} \({}^{*}\) Mean score of BLEU-1, 2, 3, and 4 score.  
\end{table} 
Table 2: _Cont._  
Figure 4: Example of radiologist reports within the whole test dataset. (**a**) Radiologist’s reports on normal CT scan; (**b**) radiologist’s reports on ICH CT scan. Unlike radiologist reports in (**b**), radiologist reports in (**a**) consist of mostly similar texts. exhibit similar or higher scores. This demonstrates that even when evaluating the model's performance using only the ICH CT scans, the proposed method in this paper provides a certain level of usability. Additionally, an important observation from the results in Table 3 is the impact of the penalty-applied loss functions on the model's performance. The goal of the penalty-applied loss functions was to prevent the model from generating None-ICH text to the ICH CT scan. As seen in Table 2, when EfficientNet-B5 utilized \(L\) w/\(p\) value 1 and \(L\) w/\(p\) value 10 for model training, the scores were lower compared to when using \(l\). However, Table 3 shows an improvement in the scores when EfficientNet-B5 utilized \(L\) w/\(p\) value 1 instead of \(l\), and achieved the highest scores among the loss functions on the ICH CT scans. Notably, in Table 3, the EfficientNet-B5 model, which recorded the highest score among the models using \(l\) in Table 2, showed the highest scores across all three loss functions (\(l\), \(L\) w/\(p\) value 1 and \(L\) w/\(p\) value 10) when evaluated on the only ICH CT scan. This suggests that utilizing penalty-applied loss functions showed a certain degree of effectiveness for