# Sequential Brain CT Image Captioning Based on the Pre-Trained Classifiers and a Language Model
## 4 Experiments
### Experiment Results  
Table 1 presents the performance evaluation results of the fine-tuned CNN classifier on the publicly available Kaggle data. VGG-19 achieved the highest precision at 0.94, while VGG-16 showed the highest recall at 0.89. All fine-tuned CNN classifiers demonstrated a high F1 score and accuracy of around 90%, indicating successful training. Next, we evaluated the performance of the four fine-tuned CNN classifiers and the GPT-2-based image captioning model on generated text. We assessed performance using N-gram-based evaluation metrics and embedding-based evaluation metrics, as shown in Tables 2 and 3. Table 2 shows the performance evaluation results for N-gram-based metrics (BLEU, METEOR, ROUGE_L, CIDEr). DenseNet-121 exhibited relatively high scores in BLEU and METEOR. While ResNet-50V2 had lower BLEU scores compared to DenseNet-121, the difference was not substantial, and it showed high scores in ROUGE_L and CIDEr. Despite VGG-16 and VGG-19 demonstrating high classification accuracy, as seen in Table 1, they exhibited lower performance in N-gram-based evaluations. Table 3 presents another set of metrics, including skip-thought, embedding average, vector extrema, and greedy matching score, which calculate the cosine similarity between the embeddings of predicted and reference sentences. The model utilizing DenseNet-121 encoder achieved the highest scores in each embedding metric: 0.54 in skip-thought, 0.71 in embedding average, 0.46 in vector extrema, and 0.63 in greedy matching. It consistently displayed high scores in N-gram-based metrics as well. This indicates that DenseNet-121's feature representation benefits from the densely connected nature of its layers, enabling it to capture complex patterns more effectively. The deeper layers contribute to the accumulation and reuse of more features, resulting in the observed high-performance outcomes [49]. \begin{table}
\begin{tabular}{c c c c c} \hline
**Classifiers** & **Precision** & **Recall** & **F1-Score** & **Acc** \\ \hline ResNet-50V2 & 0.93 & 0.87 & **0.90** & **0.92** \\ DenseNet-121 & 0.93 & 0.86 & 0.89 & 0.91 \\ VGG-16 & 0.92 & **0.89** & **0.90** & **0.92** \\ VGG-19 & **0.94** & 0.86 & **0.90** & **0.92** \\ \hline \end{tabular}
\end{table} 
Table 1: Performance comparison with four fine-tuned CNN classifiers. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multicolumn{2}{c}{**Models**} & & & & & & \\ \multicolumn{2}{c}{**(With GPT-2)**} & **B1** & **B2** & **B3** & **B4** & **B@4** & **M** & **R\_L** & **C** \\ \hline \multirow{2}{*}{ResNet-50V2} & B & 0.27 & 0.19 & 0.16 & 0.13 & 0.18 & **0.14** & **0.30** & **0.38** \\  & G & 0.25 & 0.19 & 0.15 & 0.13 & 0.18 & 0.13 & **0.30** & **0.36** \\ \hline \multirow{2}{*}{DenseNet-121} & B & **0.28** & **0.21** & **0.17** & **0.14** & **0.20** & **0.14** & 0.28 & 0.25 \\  & G & **0.28** & **0.21** & **0.17** & **0.14** & **0.20** & **0.14** & 0.29 & 0.27 \\ \hline \multirow{2}{*}{VGG-16} & B & 0.20 & 0.14 & 0.12 & 0.10 & 0.14 & 0.10 & 0.21 & 0.18 \\  & G & 0.20 & 0.15 & 0.12 & 0.10 & 0.13 & 0.09 & 0.20 & 0.16 \\ \hline \multirow{2}{*}{VGG-19} & B & 0.21 & 0.16 & 0.13 & 0.11 & 0.12 & 0.10 & 0.23 & 0.16 \\  & G & 0.21 & 0.16 & 0.13 & 0.10 & 0.12 & 0.10 & 0.23 & 0.17 \\ \hline \end{tabular}
\end{table} 
Table 2: Evaluation scores based on the N-gram metrics for the final model. (B1 to B4: BLEU, B@4: average of B1 to B4, M: METEOR, R_L: ROUGE_L, C: CIDEr, B: beam search (n = 3), G: greedy search).