## 3 Case Study: Experimental Verification and Comparison Results
### Comparison Studies
#### 3.2.2 Comparison between the Proposed Method and Non-Transfer Learning Method  
The advantage of the proposed method is that high diagnosis accuracy can be obtained even when the labeled data of the target domain for diagnosis are few. To verify this advantage, we observed the change in accuracy through a scenario in which the number of target domain training data is reduced by 10. In addition, it was compared with the accuracy when transfer learning was not used. The comparison of these results confirmed how much the diagnosis accuracy improves by using the source domain and proposed  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Input Shape**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Training Dataset**} & \multirow{2}{*}{**Classification**} \\ \cline{3-3} \cline{5-5}  & & **Labeled Target Data** & & **Source Data** \\ \hline \multirow{2}{*}{Handcrafted features} & \multirow{2}{*}{DNN (fully-connected)} & O & X & 73.80 \\  & & O & O & 50.10 \\ \hline \multirow{3}{*}{STFT image} & ResNet & O & X & 92.67 \\  & ResNet & O & O & 94.44 \\  & Proposed method & O & O & 98.33 \\ \hline \multirow{3}{*}{WPD image} & ResNet & O & X & 94.00 \\  & ResNet & O & O & 95.00 \\  & Proposed method & O & O & 97.22 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} WPD image (spectral subtraction) \\ \end{tabular} } & ResNet & O & X & 92.56 \\  & Proposed method & O & O & 96.67 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of each method.  
method. Table 4 presents the average value calculated by repeating the same process five times. Table 4 confirms that the accuracy improvement using the proposed method is more prominent when the target training data are few. We observed the average accuracy by dividing the entire range according to the number of training data into three ranges:  
* S1 for 130 to 180 data ranges with an average accuracy of 90% or more;
* S2 for 70 to 120 ranges with an average accuracy of 80%;
* S3 for 20 to 60 ranges;
* S4 for the entire 20 to 180 range.  
In S1, with many training data, the average accuracy of the proposed method was 97.5%. The average accuracy increased by 5.87% compared with 91.63% when transfer learning was not performed. In S2, the average accuracy increased by 7.26%, from 86.59% to 93.85%. In S3, with few training data, the average accuracy was 81.49% and 70.89%, respectively, and an increase of 10.6% was observed. A detailed difference of accuracy between the proposed method and non-transfer learning method is presented in Figure 6. The accuracy difference usually increases as the number of target training data decreases. Especially in cases where the number of training data is 20, 30, and 40, it is possible to observe an increase in accuracy of 14%, 13.89%, and 9%, respectively. When the transfer learning was not used, the deep learning model was not adequately trained. It was confirmed that the use of the proposed method increases the diagnosis accuracy regardless of the number of training data. The efficiency also increases when the target domain data are few.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Proposed Method**} & \multicolumn{2}{c}{**ResNet50 (Non-Transfer Learning)**} \\ \hline
**Number of Target** & **Classification** & **Max.Accuracy** & **Classification** & **Max.Accuarcy (\%)** & **Epoch** \\
**Training Data** & **Accuracy (\%)** & **Epoch** & **Accuracy (\%)** & **Max.Accuarcy** \\ \hline
180 & 98.33 & 148 & 92.67 & 497 \\
170 & 98.89 & 212 & 93.78 & 382 \\
160 & 96.44 & 96 & 91.78 & 253