## 7 Results  
In this section, we present the results obtained by the methodology for medical reports on Polydactyly X-rays, as shown in Table 2, followed by a detailed discussion. The proposed methodology demonstrated promising performance in the automatic generation of Polydactyly X-ray reports. The results of the BLEU, METEOR, and ROUGE-L metrics for the labels ABNORMAL, NORMAL, and TOGETHER indicate that the model is particularly effective in normal cases, with significantly higher scores (BLEU-1 of 0.653 and METEOR of 0.548) compared to abnormal cases (BLEU-1 of 0.426 and METEOR of 0.326). (The term \"together\" is used when we do not separate the samples into the \"abnormal\" and \"normal\" categories.) This performance difference can be attributed to the inherent complexity of describing abnormal findings and the scarcity of samples for certain pathologies, making the learning process challenging. The low variability observed in the metrics, as indicated by the standard deviations, suggests that the model exhibits robust consistency across different folds of the validation set. However, the higher variability in the BLEU-2 to BLEU-4 scores for normal cases indicates room for improvement in handling longer sequences. Overall, the model's ability to generate accurate reports for both normal and abnormal cases reinforces its applicability in the real world. For future advancements, we recommend the inclusion of more training data and the application of advanced fine-tuning techniques to enhance the model's accuracy and reliability, contributing to more efficient and precise medical diagnoses. The results observed in Table 2 suggest that the methodology achieved promising outcomes. This methodology enables the model to capture textual nuances present in Polydactly X-ray images. Furthermore, the table indicates that the model performs better for reports without abnormalities compared to those considered abnormal. This information is crucial for guiding decisions on how the methodology can assist medical professionals. A more in-depth analysis can be made from Figure 10, where the box plot reveals a consistent distribution of the evaluation metrics (BLEU-1, BLEU-2, BLEU-3, BLEU-4, METEOR, and ROUGE-L) across the 10 folds. The relatively high median in all metrics, considering that all are above 0.50 when we analyze the entire test set under \"All Labels\", indicates consistency in the quality of the predictions. The presence of outliers, on the other hand, reflects variations in certain iterations of cross-validation. However, the overall distribution of the metrics, including METEOR and ROUGE-L, remains stable across the folds, suggesting a general consistency in the model's ability to generate automatic medical reports. These results indicate that our method maintains uniform and reliable performance, establishing a solid foundation for the reliability and robustness of the proposed model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & **METEOR** & **ROUGE-L** \\ \hline AB & 0.426 \(\pm\) 0.030 & 0.340 \(\pm\) 0.031 & 0.303 \(\pm\) 0.034 & 0.282 \(\pm\) 0.037 & 0.326 \(\pm\) 0.022 & 0.271 \(\pm\) 0.027 \\ N & 0.653 \(\pm\) 0.072 & 0.570 \(\pm\) 0.102 & 0.509 \(\pm\) 0.094 & 0.503 \(\pm\) 0.094 & 0.548 \(\pm\) 0.068 & 0.504 \(\pm\) 0.078 \\ T & 0.516 \(\pm\) 0.018 & 0.432 \(\pm\) 0.026 & 0.386 \(\pm\) 0.020 & 0.370 \(\pm\) 0.019 & 0.414 \(\pm\) 0.018 & 0.364 \(\pm\) 0.021 \\ \hline \hline \end{tabular}  
AB is abnormal, N is normal and T is together.  
\end{table} 
Table 2: Average metric results for each fold along with the standard deviation. Figure 10: Box plot of the results of the automatic generation of medical reports with the metrics BLEU-1, BLEU-2, BLEU-3, BLEU-4, METEOR, and ROUGE-L across the 10 folds, for both the “anormal” and “normal” classes, as well as for the combination of both “All Labels”.