## 3 Results and Discussion
### Detection of Protein Overexpression in Gels Using the IPBBIS Algorithm
#### 3.3.7 IPBBIS Study on the Image Dataset Using the Confusion Matrix  
After verifying that the IPBBIS method can automatically identify the level of overexpression in each sample, the polyacrylamide gel image dataset was divided into homogeneous and heterogeneous gels to test their efficiency. The so-called homogeneous gels (dataset of 44 gels with a total of 669 samples) showed similar characteristics, such as the same color and quality, and no imperfections, such as \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline Lane & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 \\ \hline Concentration & 2.0 & 0.0 & 14.5 & 9.5 & 27 & 18 & 30 & 18.5 & 26 & 14 \\ mg/mL & 10.38 & 0.0 & 20.55 & 18.67 & 27.34 & 22.87 & 28.82 & 23.08 & 27.26 & 20.11 \\ Area Manual & 513.62 & 0.0 & 830.25 & 680.12 & 1373.80 & 830.00 & 1061.50 & 934.50 & 983.50 & 869.25 \\ Area K-means & 495.50 & 0.0 & 969.62 & 933.00 & 1457.20 & 1132.20 & 1336.20 & 1134.90 & 1422.10 & 993.13 \\ segmentation & 535.75 & 0.0 & 1189.8 & 1023.4 & 1646.8 & 1318.2 & 1557.1 & 1269.1 & 1585.9 & 1174.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Different samples Data of GPN concentrations. Figure 15: Image profiling of Figure 14D, based on binarized image segmentation in the region of interest. (**A**) lane 2, (**B**) lane 4, (**C**) lane 5, (**D**) lane 6, (**E**) lane 7, (**F**) lane 8, (**G**) lane 9, (**H**) lane 10, and (**I**) lane 11.  
breaks or distortions due to incorrect preparation. In this research, a confusion matrix defines true positives (TP) as cases where IPBBIS correctly detected the lane or protein band. False negatives (FN) occurred when the lane existed but was not found, false positives (FP) when the lane did not exist but was detected, and true negatives (TN) when the lane did not exist and was also not detected. The confusion matrix obtained after analyzing the 669 samples is presented in Table 5. The precision obtained was 0.985052 (see Table 6, the precision of homogeneous gels). Then, the analysis was repeated using the IPBBIS method on the heterogeneous gels, a total of 90 gels with different conditions, which included distorted (smiley face effect), broken, or incorrectly stained gels. In total, 1561 samples were analyzed for GPN protein overexpression. The accuracy of this analysis was measured using the same confusion matrix with the TP, FN, TN, and FP values defined above for the homogeneous gels. The confusion matrix is shown in Table 7, and the precision obtained was 0.91736 (see Table 6, the precision of heterogeneous gels). This precision was lower than that obtained with homogeneous gels since the SDS-PAGE gels analyzed present characteristics that make them different, such as breaks, distortions due to incorrect preparation, or insufficient Coomassieie blue staining. 3.8 Functionality of the Methods Analyzed to Find GPN Protein Overexpression: IPBBIS, Manual Area Calculation, Area Calculation by K-Means Segmentation, and Area Calculation by Otsu Segmentation  
To calculate protein overexpression, the areas of each of the GPN protein bands expressed at different concentrations in Figure 14D were measured manually by outlining the contour of the band and using the K-means segmentation and Otsu segmentation techniques (see Figure 16) and then compared with the measurement performed by the IPBBIS method. \begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**Accuracy of Homogeneous Gels**} & \multicolumn{2}{c}{**Accuracy of Heterogeneous Gels**} \\ \hline
0.985052 & 0.91736 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The Table shows the accuracy results obtained from the confusion matrices in Tables 5 and 7. \begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Predicted**} \\ \hline  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{Real} & Positive & TP = 310 & FN = 8 \\  & Negative & FP = 2 & TN = 349 \\ \hline \hline \end{tabular}
\end{table}