## 4 Results
### SCH tympanic Membrane Dataset  
The training of the SCH tympanic membrane dataset used the proposed model, MLP, and EffclientNet B7, which connected drop connect. For better performance, the model was fine-tuned with the weight of the noisy student [39]. The weight of the noisy student refers to the additional training of the JFT-300M dataset on the ImageNet large-capacity dataset using the noisy student training method that divides the teacher and student model into non-label training. The experimental results of the tympanic membrane disease dataset are as follows. As a result of training all 100 epochs, the weight at 50 epochs showed the highest performance, and the validation and test performance of the corresponding weight are shown in Table 11. Table 12 shows the inference time for measuring the performance of the test set received from SCH with the previous weight. Figure 6 shows a visualization of the correct answer prediction results for each class using Grad-CAM [40]. The experimental results of the pediatric hearing dataset are as follows. As a result of learning all 300 epochs, 215 epochs showed the highest performance, and the validation and test performance at these epochs are shown in Table 13. The inference time measuring the performance of the test set received from SCH is shown in Table 14, and the result of visualizing the predicted result for each dB with a difference of less than 5 between the predicted value and GT in Grad-CAM is shown in Figure 7. In the case of Table 13, additional benchmarks were executed with the same data to evaluate the regression performance of the proposed model. As a result, like the classification part, it was confirmed that the performance of our model was the most compliant among the comparative models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Average** & **Average** & **Average** \\
**Accuracy** & **Sensitivity** & **Specificity** \\ \hline MobileNet V3 & 88.91\% & 77.81\% & 92.60\% \\ DenseNet 201 & 91.88\% & 83.75\% & 94.58\% \\ Vanilla EfficientNet B7 & 92.34\% & 84.69\% & 94.90\% \\ ConvNeXt & 93.28\% & 86.56\% & 95.52\% \\ Ours (EfficientNet B7-based) & 93.59\% & 87.19\% & 95.73\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The quantitative comparison results on the open-access tympanic membrane dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Mean Absolute Error** & **Mean Squared Logarithmic Error** \\ \hline Validation Hearing & 6.9801 & 0.2798 \\ \hline MobileNet V3 & 7.5890 & 0.3260 \\ ConvNeXt & 7.4721 & 0.3005 \\ Ours & 6.8678 & 0.2887 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The quantitative pediatric hearing result on SCH tympanic membrane disease dataset. Figure 6: Visualization comparison with Grad-CAM on SCH tympanic membrane disease dataset. Unlike the average performance in the experiment, both the classification model and the regression model had the problem of lowering performance in a specific class or dB. As shown in Figures 8 and 9, many such problems were seen in the cholesteatoma for the tympanic membrane disease model and 60 dB for the pediatric hearing model. This seems to be a problem caused by a data imbalance, and it was not completely overcome by a method such as class weight in the training process. Figure 8: Correct and incorrect classification of Cholesteatoma. (Bold green letters indicate the correct prediction, and bold red letters indicate the incorrect prediction). Figure 7: Visualization of the pediatric hearing using Grad-CAM. Figure 9: Small prediction error and large prediction error when GT is 60 dB. (Bold green letters indicate the correct prediction, and bold red letters indicate the incorrect prediction). Figure 10: Visualization comparison of box plot results on open-access tympanic membrane disease model.