# SimMolCC: A Similarity of Automatically Detected Bio-Molecule Clusters between Fluorescent Cells
## 5 Experiments
### Experiment I
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Step 4(c)**} & \multicolumn{2}{c}{**Step 4(d) w \"1 st\"**} & \multicolumn{2}{c}{**Step 4(d) w \"3 rd\"**} \\
**Cell \#** & **mAP** & **F1-Score** & **mAP** & **F1-Score** & **mAP** & **F1-Score** \\ \hline Cell \#1 & 0.288 & 0.186 & 1.000 & 0.019 & 1.000 & 0.019 \\ Cell \#2 & 0.604 & 0.284 & 0.741 & 0.114 & 0.698 & 0.460 \\ Cell \#3 & 0.309 & 0.140 & 0.554 & 0.130 & 0.554 & 0.127 \\ Cell \#4 & 0.385 & 0.166 & 0.609 & 0.152 & 0.491 & 0.229 \\ Cell \#5 & 0.364 & 0.261 & 0.833 & 0.019 & 0.506 & 0.119 \\ Cell \#6 & 0.478 & 0.135 & 1.000 & 0.044 & 0.584 & 0.411 \\ Cell \#7 & 0.440 & 0.177 & 0.806 & 0.031 & 0.747 & 0.041 \\ Cell \#8 & 0.573 & 0.256 & 0.735 & 0.300 & 0.641 & 0.458 \\ Cell \#9 & 0.593 & 0.216 & 0.686 & 0.055 & 0.695 & 0.423 \\ Cell \#10 & 0.424 & 0.192 & 0.975 & 0.099 & 0.921 & 0.122 \\ Cell \#11 & 0.536 & 0.283 & 1.000 & 0.015 & 0.639 & 0.404 \\ Cell \#12 & 0.393 & 0.233 & 0.729 & 0.069 & 0.729 & 0.069 \\ Cell \#13 & 0.491 & 0.337 & 0.833 & 0.028 & 0.618 & 0.283 \\ Cell \#14 & 0.603 & 0.300 & 1.000 & 0.037 & 0.899 & 0.123 \\ Cell \#15 & 0.609 & 0.415 & 0.565 & 0.088 & 0.698 & 0.458 \\ \hline Avg. (\(\mu\)) & 0.472 & 0.239 & **0.804** & 0.080 & 0.695 & **0.250** \\ SD (\(\sigma\)) & 0.107 & 0.075 & 0.157 & 0.072 & 0.144 & 0.165 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The mAP @ IoU = 0.5 and F1-score @ IoU = 0.5 of the proposed methods of Steps 4(c) and 4(d) flagged as “1 st” or “3 rd” for each input of 15 average fluorescent cell images with the size of kernel, kernel_size\({}^{2}\), for OpenCV’s Laplacian operator [69], cv2. [PERSON](), at Step 2(b). Figure 13: The ground truth, Step 4(c), and Step 4(d) flagged as “1 st” or “3 rd” of an input image (fluorescent cell #6 or #14) for automatic detection of bio-molecule clusters with the size of kernel, kernel_size\({}^{2}=1\), for OpenCV’s Laplacian operator [69], cv2. [PERSON](), at Step 2(b). (a) Cell #6; (b) Cell #14. Laplacian operator [69], cv2.Laplacian(), at Step 2(b), respectively. An analysis of the figures provides the following findings:  
* The dependency of mAP @ IoU on the size of kernel is more stable, while the dependency of F1-score @ IoU on the size of kernel is less stable. More specifically, the dependency of recall @ IoU on the size of kernel is less stable than the dependency of precision @ IoU on the size of kernel. It might be caused by the limitations of Dataset I. The future work will make the dataset larger and more ground-true. * The curve of mAP over IoU is the best when the size of kernel is set to 1 and the 2 nd best when the size of kernel is set to 5, and then, the larger the size of kernel is, the slightly worse the curve of mAP over IoU is. * The curve of F1-score over IoU is the best when the size of kernel is set to 5 and the 2 nd best when the size of kernel is set to 3, and then, the larger the size of kernel is, the worse the curve of F1-score over IoU is. * Overall, the curves of both mAP over IoU and F1-score over IoU come in a slamming 1 st place when the size of kernel is set to 5. Figure 15 shows the dependency of the mAP @ IoU and F1-score @ IoU of the proposed methods of Step 4(d) flagged as \"3 rd\" and \(\texttt{kernel\_size}=5\) (which is manually optimized as the overall finding of the dependency analysis of mAP @ IoU and F1-score @ IoU on the size of kernel) on the number of sampled histograms, \(n\), for Step 4(d), respectively. An analysis of the figures provides the following findings:  
* The dependency of mAP @ IoU on the number of sampled histograms is more stable, while the dependency of F1-score @ IoU on the number of sampled histograms is less stable. More specifically, the dependency of recall @ IoU on the number of sampled histograms is less stable than the dependency of precision @ IoU on the number of sampled histograms. This might be caused by the limitations of Dataset I. The future work will make the dataset larger and more ground-true.