## 4 Experiments
### BLEU and CIDEr Score  
In order to verify the effect of the proposed model on image description generation, three comparative experiments were undertaken in this part. Comparison experiment 1: The model presented in this paper was compared with baseline models such as Multimodal RNN, DeepVS, and Google NIC [28]. Each model was trained and tested on our spine X-ray dataset, and the test and comparison results are shown in Table 3.  
Judging from the results, the model proposed in this paper was compared with Multimodel RNN, DeepVS, Google NIC, and Soft-Attention and Hard-Attention models using attention mechanisms that have a great improvement on the BLEU index to evaluate the smoothness and accuracy of generated sentences, and the CIDEr index to reflect the diversity of image description generation. Comparison experiment 2: This model was compared with the models mentioned in the related work in Section 2 of this paper in our spinal X-ray image dataset. The comparison results are shown in Table 4.  
Figure 6: (a) Impact of learning rate on BLEU-4 evaluation indicators; (b) impact of learning rate on CIDEr evaluation indicators. The comparison results on the spinal X-ray image dataset show that compared with the four models, the proposed model had considerable improvement in all evaluation indexes of BLEU-2, BLEU-3, BLEU-4, and CIDER, except the BLEU-1 index. Comparison experiment 3: In order to demonstrate the effect of simplified explanatory sentences, we showed the BLEU and CIDER scores of the trained model under the original diagnosis report and the simplified sentence report, respectively. The scores of BLEU and CIDEr are shown in Table 5. Non-scoliosis samples were not included in the test set because non-scoliosis images would automatically be described as empty sentences, perfectly repeating the original label, which would inflate the score. It can be seen from the table that the diagnostic text generated by the model trained with the simplified diagnostic report had much higher scores in both the BLEU1-4 index and the CIDEr index than that generated by the model trained with the original report, which confirms that simplifying complex diagnostic reports can improve the model's ability to reproduce sentences and obtain more accurate spinal image description. During \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods of Related Work** & **Datasets** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & **CIDEr** \\ \hline Reference [17] & Spine X-Ray & 0.695 & 0.390 & 0.384 & 0.291 & 0.812 \\ \hline Reference [20] & Spine X-Ray & 0.789 & 0.483 & 0.365 & 0.259 & 0.451 \\ \hline Reference [21] & Spine X-Ray & 0.673 & 0.489 & 0.355 & 0259 & 0.766 \\ \hline Reference [23] & Spine X-Ray & 0.467 & 0.361 & 0.298 & 0.257 & 0.808 \\ \hline Ours & Spine X-Ray & 0.763 & 0.592 & 0.445 & 0.360 & 0.847 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The evaluation results of this model are compared with those of other models in related work on spinal X-ray image datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Baseline Model** & **Datasets** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & **CIDEr** \\ \hline \multirow{2}{*}{MultimodalRNN} & Spine X-Ray & 0.670 & 0.490 & 0.350 & 0.250 & 0.760 \\ \hline \multirow{2}{*}{DeepVS} & Spine X-Ray & 0.625 & 0.450 & 0.321 & 0.230 & 0.660 \\ \hline \multirow{2}{*}{Google NIC} & Spine X-Ray & 0.665 & 0.461 & 0.329 & 0.246 & 0.813 \\ \hline \multirow{2}{*}{Soft-Attention} & Spine X-Ray & 0.707 & 0.492 & 0.344 & 0.243 & 0.802 \\ \hline \multirow{2}{*}{Hard-Attention} & Spine X-Ray & 0.718 & 0.504 & 0.357 & 0.250 & 0.780 \\ \hline \multirow{2}{*}{Ours} & Spine X-Ray & 0.763 & 0.592 & 0.445 & 0.360 & 0.847 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the evaluation results of this model and the baseline model on the spinal X-ray image dataset.