## 3 Experiment Results
### Experiment Result with the Test Data Consisted of ICH CT Scan
\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 & 0.01 & 0.03 & 0.08 & 0.51 \\  & ResNet-50 + B & 0.09 & 0.08 & 0.20 & 0.61 \\  & ResNet-50 + T & 0.04 & 0.05 & 0.09 & 0.59 \\  & EfficientNet-B5 + G & 0.06 & 0.09 & 0.16 & 0.58 \\  & EfficientNet-B5 + B & **0.16** & 0.13 & **0.28** & **0.63** \\  & EfficientNet-B5 + T & 0.09 & 0.10 & 0.17 & **0.63** \\  & DenseNet-201 + G & 0.06 & 0.09 & 0.09 & 0.56 \\  & DenseNet-201 + B & 0.12 & **0.16** & 0.10 & 0.62 \\  & DenseNet-201 + T & 0.08 & 0.10 & 0.07 & 0.60 \\  & ConvNeXt-S + G & 0.01 & 0.02 & 0.06 & 0.53 \\  & ConvNeXt-S + B & 0.01 & 0.02 & 0.06 & 0.53 \\  & ConvNeXt-S + T & 0.03 & 0.03 & 0.07 & 0.55 \\ \hline \multirow{8}{*}{\(L\) w/\(p\) value 1} & ResNet-50 + G & 0.15 & 0.05 & 0.11 & 0.58 \\  & ResNet-50 + B & 0.14 & 0.10 & 0.22 & 0.62 \\  & ResNet-50 + T & 0.04 & 0.06 & 0.09 & 0.60 \\  & EfficientNet-B5 + G & 0.07 & 0.10 & 0.17 & 0.62 \\  & EfficientNet-B5 + B & **0.17** & **0.17** & **0.30** & **0.68** \\  & EfficientNet-B5 + T & 0.10 & 0.11 & 0.18 & 0.65 \\  & DenseNet-201 + G & 0.04 & 0.08 & 0.13 & 0.54 \\  & DenseNet-201 + B & 0.13 & 0.15 & 0.27 & 0.62 \\  & DenseNet-201 + T & 0.07 & 0.10 & 0.16 & 0.60 \\  & ConvNeXt-S + G & 0.01 & 0.02 & 0.06 & 0.53 \\  & ConvNeXt-S + T & 0.04 & 0.05 & 0.09 & 0.58 \\ \hline \multirow{8}{*}{\(L\) w/\(p\) value 10} & ResNet-50 + G & 0.03 & 0.05 & 0.11 & 0.57 \\  & ResNet-50 + B & 0.12 & 0.10 & 0.24 & 0.63 \\  & ResNet-50 + T & 0.04 & 0.06 & 0.09 & 0.60 \\  & EfficientNet-B5 + G & 0.07 & 0.08 & 0.16 & 0.61 \\  & EfficientNet-B5 + B & **0.18** & **0.14** & **0.29** & **0.66** \\  & EfficientNet-B5 + T & 0.11 & 0.10 & 0.18 & 0.65 \\  & DenseNet-201 + G & 0.06 & 0.09 & 0.16 & 0.58 \\  & DenseNet-201 + B & 0.15 & **0.14** & 0.28 & 0.63 \\  & DenseNet-201 + T & 0.08 & 0.10 & 0.16 & 0.63 \\  & ConvNeXt-S + G & 0.01 & 0.05 & 0.05 & 0.52 \\  & ConvNeXt-S + B & 0.02 & 0.07 & 0.07 & 0.51 \\  & ConvNeXt-S + T & 0.03 & 0.08 & 0.08 & 0.57 \\ \hline \hline \end{tabular} \({}^{*}\) Mean score of BLEU-1, 2, 3, and 4 score.  
\end{table} 
Table 3: Experimental results with test data consisted of an ICH CT scan. G denotes Greedy Search, B denotes Beam Search, T denotes Top-k Sampling. Highest score for each metric within the loss function is highlighted with bold text. the EfficientNet-B5 model. Furthermore, the effect of penalty-applied loss functions is more shown in the ResNet-50 model. Comparing the scores of the ResNet-50 model between \(l\) and \(L\) w/\(p\) value 1 in Table 3, we observed a large improvement in BLEU scores for Greedy Search and Beam Search strategy, as well as slight score improvements for other metrics when \(l\) was utilized. The effect of penalty-applied loss functions can also be observed in the model-generated text shown in Table 4.  
There are also some noteworthy points in the results of the DenseNet-201 model. In Table 2, the scores of DenseNet-201 + G and DenseNet-210 + B models did not show the highest performance compared to other models, but they recorded scores close to the models with the highest scores within their loss function groups. Furthermore, in Table 3, the DenseNet-210 + B model demonstrated scores that were either close to, equal to, or higher than the models with the highest scores within their loss function groups in some metrics.