## 3 Experiments and Results
### Performance of the ResNet-50 Classification Model  
We utilized 10,410 TURP surgery images as training/validation datasets, and the others were used for testing datasets. However, the TURP surgery datasets were difficult to access, and a serious imbalance issue within the datasets in this research could lead to undesirable results. Such an issue could mean that the number of framed images with the red light is inadequate for the training. To solve this problem, a technique of data augmentation could be implemented; thus, we used flip, rotation, and shift to augment the red light images. The respective numbers of the images with the red and non-red lights were prepared to be approximately 3000 for the training. As shown in Table 4, our experimental results are evaluated by the indexes of accuracy, specificity, and sensitivity. Accuracy is  
Figure 8: The examples of frames shown per 0.1 s in a video of the prostate surgery (e.g., the meaning of 0.1 s represents the surgery clips at 0.1 s).  
\begin{table}
\begin{tabular}{c c} Training/Validation & 10,410 images \\ Testing & 15,615 images \\ \end{tabular}
\end{table}
Table 3: The dataset ratio of the transurethral resection of the prostate (TURP) surgery.  
the global corrective percentage of classification identification, specificity is the corrective proportion of the image identified without the red light, and sensitivity indicates the corrective proportion of the image identified with the red light. They are defined in Equations (2)-(4), and the definition of the classification metrics is shown in Table 4. Moreover, the following are the definitions of TP, TN, FN, and FP: TP is the number of correctly classified images with the red light, TN is the number of correctly classified images with the non-red light, FN is the number of falsely classified images with the red light, and FP is the number of falsely classified images with the non-red light [48; 49]. To validate the performance of the training process, two indicators of training accuracy and loss function are used to evaluate the results of the self-predicted data. The results of the training accuracy and loss function for the ResNet-50 model are both convergent to the best of the fit line, which are shown in Figure 9.  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]  
\[\text{Specificity}=\frac{TN}{FP+TN} \tag{3}\]  
\[\text{Sensitivity}=\frac{TP}{TP+FN} \tag{4}\]  
To evaluate the effectiveness of the ResNet-50 model, this study used a confusion matrix to show the classification performance of the prostate images with the red and non-red lights. The confusion matrix for testing the TURP surgery datasets is shown in Table 5. The classification accuracy, sensitivity, and specificity from the confusion matrix result achieved 97%, 98%, and 94%, respectively, for testing datasets for high accuracy, corrective robustness, and global correctness, as shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{5}{c}{**Actual Class Label**} \\ \hline Prediction & & **Yes** & **No** \\ Class & **Yes** & TP & FP \\ Label & **No** & FN & TN \\ \hline \multicolumn{5}{c}{**Sensitivity**} & **Specificity** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Definition of classification metrics.  
Figure 9: ResNet-50 model training results. (**a**) The accuracy of self-predicted data. (**b**) The loss function of self-predicted data.  
To realize the false classification circumstances, the images of FN and FP in the case of testing the TURP surgery datasets listed in Table 5 were picked for investigation. Figure 10(a1,a2) illustrates the FN images, which reveal that the large dark color blood region is misjudged for the red light pattern. Figure 10(b1,b2) presents the FP images, which reveal that the small red light area is ignored or mistaken for the non-red light image.