## 3 Experimental Results
The sample deviation is calculated using the following equation:  
\[\text{sample deviation}=\sqrt{\frac{\sum_{1}^{n}(x_{i}-\overline{\mathrm{x} })^{2}}{n-1}}. \tag{2}\]  
The 95% confidence interval, in terms of percentage, is used for our experiment. In additional, for better visualization of the data, the results have been plotted in Figure 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Surgical Instrument** & **Faster R-CNN, MAP** &
\begin{tabular}{c} **Faster R-CNN** \\ **(COCO), MAP** \\ \end{tabular} & **Mask R-CNN, MAP** & **Multi-Box SSD, MAP** \\ \hline clamp\_pliers & 99.63\% \(\pm\) 0.2\% & 96.34\% \(\pm\) 2.6\% & **100\%** \(\pm\) 0\% & 93\% \(\pm\) 2.7\% \\ \hline diagonal\_pliers & **99.74**\% \(\pm\) 0.2\% & 88.76\% \(\pm\) 6.9\% & 99.26\% \(\pm\) 0.2\% & 94.52\% \(\pm\) 4.2\% \\ \hline forceps & **99.65**\% \(\pm\) 0.2\% & 86.85\% \(\pm\) 0.3\% & 99.51\% \(\pm\) 0.2\% & 88.15\% \(\pm\) 1.3\% \\ \hline gunshaped\_forceps & 89.19\% \(\pm\) 3\% & 73.36\% \(\pm\) 5.4\% & **99.21**\% \(\pm\) 0.3\% & 93.82\% \(\pm\) 3.9\% \\ \hline gunshaped\_forceps+ & **100.00**\% \(\pm\) 0\% & 98.86\% \(\pm\) 0.5\% & 99.5\% \(\pm\) 0.3\% & 92.87\% \(\pm\) 4.2\% \\ \hline long\_scalpel & **99.23**\% \(\pm\) 0.3\% & 81.08\% \(\pm\) 12.6\% & 99.21\% \(\pm\) 0.3\% & 94.94\% \(\pm\) 3.7\% \\ \hline medium\_scalpel & **100.00**\% \(\pm\) 0\% & 98.33\% \(\pm\) 0.7\% & 99.49\% \(\pm\) 0.3\% & 100.00\% \(\pm\) 0\% \\ \hline mosquito\_clamp & 93.82\% \(\pm\) 0.2\% & 79.54\% \(\pm\) 10\% & **99.21**\% \(\pm\) 0.7\% & 89.91\% \(\pm\) 0.3\% \\ \hline mosquito\_scissors & **99.78**\% \(\pm\) 0.2\% & 98\% \(\pm\) 0.3\% & 99.07\% \(\pm\) 0.2\% & 96.33\% \(\pm\) 1.1\% \\ \hline scissors & 97.85\% \(\pm\) 0.6\% & 63.80\% \(\pm\) 5.3\% & **100**\% \(\pm\) 0\% & 87.66\% \(\pm\) 11.3\% \\ \hline short\_scalpel & **99.60**\% \(\pm\) 0.2\% & 87.25\% \(\pm\) 2.1\% & 99.14\% \(\pm\) 0.3\% & 99.20\% \(\pm\) 0.5\% \\ \hline spring\_needle\_holders & 93.57\% \(\pm\) 0.2\% & 96.97\% \(\pm\) 3\% & **98.01**\% \(\pm\) 0.5\% & 87.09\% \(\pm\) 7.1\% \\ \hline steel\_push & 99.78\% \(\pm\) 0.3\% & 100\% \(\pm\) 0\% & **100**\% \(\pm\) 0\% & 94.79\% \(\pm\) 2.8\% \\ \hline towel\_clamp & 86.26\% \(\pm\) 3.9\% & 78.02\% \(\pm\) 6.8\% & **93.18**\% \(\pm\) 2.1\% & 85.24\% \(\pm\) 10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification results of the networks trained used surgical instrument data with a 95% confidence interval. Figure 7: MAP with 95% confidence interval. Figure 8 show examples of correctly identified instrument by the Mask RCNN network. Figure 9 shows a few examples of classified images of a clamp plier and a mosquito clamp that were correctly identified by the Mask R-CNN classifier but were mis-identified, at least once, by the original Faster R-CNN (not transfer learned) classifier. The performances of the trained Faster R-CNN and the Mask R-CNN are comparable for most of the instruments, as expected. Although these classifiers took longer to detect and recognize the surgical instrument(s), they are more accurate than the SSD and the transfer learned Faster R-CNN classifers in most of the categories. The accuracies of classification for the towel clamp are not high for all networks, due to mis-identifying it as one of the other types of clamp, such as the mosquito clamp. In terms of the average MAP for all the instruments, the Mask R-CNN classifier has the highest value of 98.94%, which is followed by 96.97% achieved by the Faster R-CNN classifier without transfer learning, while the SSD classifier achieved an average MAP value of 92.61%, and the Faster R-CNN classifier using transfer learning from the COCO dataset had only reached an MAP of 87.68%. This implied that the method of transfer learning, which seeks to reduce training time, may not be suitable for the classifiers for surgical instruments--at least classifiers trained using the COCO dataset. Figure 8: Some examples of correct results by the trained Mask RCNN network.