## 3 Experiment Results
### Examples of Generated Text  
Table 4 displays the text generated by the EfficientNet-B5 + B model, which achieved the highest scores in Tables 2 and 3. For both the first and second rows of the ICH CT scan, we can see that applying \(L\) w/\(p\) value 1 and \(L\) w/\(p\) value 10 to the model helps generate sentences closer to the reference texts compared to applying \(l\). Simultaneously, the texts generated by the models using \(L\) w/\(p\) value 1 and \(L\) w/\(p\) value 10 provide more detailed information than the model that applied only \(l\) for both ICH CT scans. For example, in the case of the first row, the model applying \(l\) does not provide information on \"midline shifting focal lacunar infarction in the left internal capsule.\" However, \(L\) w/\(p\) value 1 includes the information of \"midline shifting,\" and \(L\) w/\(p\) value 10 incorporates \"lacunar \begin{table}
\begin{tabular}{|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|} \hline \multicolumn{1}{|c|}{**Reference Text**} & \multicolumn{1}{c|}{\(l\)} & \multicolumn{1}{c|}{\(L\) w/\(p\) Value 1} & \multicolumn{1}{c|}{\(L\) w/\(p\) Value 10} \\ \hline diffuse left cerebral subunit & & diffuse left cerebral subunit & diffuse right cerebral subunit \\ \hline hemorrhage midline shifting & diffuse right cerebral & hemorrhage midline shifting & hemorrhage midline shifting \\ focal lacunar infarction in left internal capsule & subdural hemorrhage in right & subdural hemorrhage in right & multiple old lacunar \\  & & sylvian cistern & infarctions in both basal ganglia and right thalamus \\ \hline sdh, right cerebral hemisphere & & sdh, right cerebral hemisphere \\ small vessel disease with & sdh in right fronto parietal & sdh, left fronto parietal & small vessel disease with \\ lacunar infarctions & otherwise no demonstraabled & otherwise no demonstraabled & lacunar infarctions \\ no demonstraabled & abnormal finding & Abnormal finding & otherwise no demonstraabled \\ abnormal finding & & abnormal finding & abnormal finding \\ \hline unremarkable finding of & unremarkable finding of & unremarkable finding of & unremarkable finding of & unremarkable finding of \\ brain parenchyma and & brain parenchyma and & brain parenchyma and & brain parenchyma and \\ cerebrospinal fluid space & cerebrospinal fluid space & cerebrospinal fluid space & cerebrospinal fluid space \\ \hline unremarkable finding of & unremarkable finding of & & \\ brain parenchyma and & brain parenchyma and & diffuse brain atrophy & diffuse brain atrophy \\ cerebrospinal fluid space & cerebrospinal fluid space & & \\ \hline \end{tabular}
\end{table} 
Table 4: EfficientNet-B5 + B generated texts with all test data. Top 2 rows are from the ICH CT scan, and bottom 2 rows are from the normal CT scan. Columns except reference text show a change in the generated text with the change in loss function and the penalty value. Effect of the loss function with penalty is shown in each row as the additional information in the generated text increases while it brings the generated text closer to the reference text. Overlapping tokens are highlighted with the colors. infarctions\". Additionally, looking at the text in the second row, we can see that the text from the model with \(L\) w/_p_ value 10 applied is identical to the reference text, confirming that the application of \(L\) w/_p_ value 1 and \(L\) w/_p_ value 10 had a positive effect. The spatial information consideration of 3D-CNN is indeed evident in Table 4. In the first row of the reference text in Table 4, we can observe information regarding \"diffuse subdural hemorrhage.\" Subdural hemorrhage refers to bleeding between the dura mater and the arachnoid membrane, and \"diffuse subdural hemorrhage\" signifies hemorrhage that spreads widely between these layers. To determine whether hemorrhage is diffuse or not, it is necessary to examine the spatial information in the CT scan.