## 4 Discussion  
From the experimental data above, although the performance of the Faster R-CNN and the Mask R-CNN classifiers are almost the same in most categories, in the gunshaped forceps category, the Mask R-CNN classifier outperformed the Faster R-CNN, on average, by about 10%, which is significant. However, the architecture of the Faster-RCNN is almost the same as that of the Mask R-CNN, and their main difference in the architectures would only result in the variation of the positions of the bounding boxes, so it can be claimed that a more accurate bounding box will result in a better classification in some cases. However, observing the results shown above in Figure 9, the differences in the bounding boxes generated by the two classifiers is that the bounding boxes generated by the Mask R-CNN classifier show consistent and better fits around the instrument to be identified. On the other hand, the boxes generated by the Faster R-CNN classifier are not as consistent; i.e., they are sometimes looser and sometimes tighter than those generated by the Mask R-CNN classifier. However, it is likely that bounding boxes may not be the only difference that caused the 10% difference in the gunshaped forceps category. The positions and poses of the instrument may also contribute to the differences in classification result. In this study, only images extracted from clips were used in the experiments rather than the clips themselves, so the frames-per-second (FPS) performances of these networks may only be estimated by the amount of time it took to detect and classify each image. The single-shot method clearly outperforms the other architectures in this area; the multi-box SSD method could probably do around 16 to 20 FPS, while the other two-shot architectures could do around 5 to 6 FPS. However, since this paper considers surgical videos in which surgical instruments are not considered as fast-moving objects, 5 to 6 FPS in processing time can be considered as near real time. In addition, in examining the MAPs with confidence intervals in Table 2 and Figure 7, the high ends of the single-shot method are comparable with the top performers. This observation may imply that a good design of single-shot deep learning architecture may equal the top performers in accuracy while achieving more FPS in real-time processing for actual video processing. Another limitation of this study is that the extracted images were selected in the case where each instrument is clearly separated by itself, but in the surgical video clips, there are many cases where the instruments overlay each other. The performances of top performers may not be as expected in these cases. These cases and others will have to be included in future studies. In terms of the experiments performed in this study, several observations can also be made from the data above. First, the performances of these networks are generally good on scalpel-type instruments but not as well on clamp-type instruments. This result may be due to the possibility that different types of clamps could exhibit similar features. The second observation is that the Faster R-CNN-based networks, including Mask R-CNN, with the region proposal network, are less likely to fail to detect the presence of a surgical instrument in comparison with the single-shot SSD network. However, whether this observation is true for all single-shot networks, such as different versions of the YOLO (You Only Look Once) network [24; 25; 26], have to be investigated in a different study. The third observation is that, as expected, the single-shot SSD network is faster in reaching a result than the two-shot Faster R-CNN. If real-time processing is a critical requirement, then one-shot networks should be investigated. However, though sometimes the SSD network did fail in detecting the instruments, the SSD classifier was able to have a slightly better performance on the gunshaped forceps than the Faster R-CNN classifier but not on the modified gunshaped forceps (gunshaped forceps+).