## 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
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \hline Concentration mg/mL & 0.00 & 0.07 & 0.32 & 0.47 & 0.48 & 0.60 & 0.62 & 0.87 & 0.90 & 1.00 \\ \hline Lane & 3 & 2 & 5 & 11 & 4 & 7 & 9 & 10 & 6 & 8 \\ ROI-GPN & 0.00 & 0.36 & 0.65 & 0.70 & 0.71 & 0.79 & 0.80 & 0.95 & 0.95 & 1.00 \\ Manual Area & 0.00 & 0.37 & 0.50 & 0.63 & 0.60 & 0.60 & 0.68 & 0.72 & 1.00 & 0.77 \\ K-means segmentation & 0.00 & 0.34 & 0.64 & 0.68 & 0.67 & 0.78 & 0.78 & 0.98 & 1.00 & 0.92 \\ Otsu segmentation Area & 0.00 & 0.33 & 0.62 & 0.71 & 0.72 & 0.80 & 0.77 & 0.96 & 1.00 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Normalization of the data from Table 8.  
These results demonstrate that the developed IPBBIS profiling allows for discovering overexpression and correctly identifying the level of overexpression related to GPN protein concentration. On the other hand, the manual methods, K-means segmentation and Otsu segmentation, presented errors in the measurements. In addition, the IPBBIS method can analyze the ROI region without cutting out each of the samples present in the gel. In contrast, manual techniques, like K-means segmentation and Otsu segmentation, require the samples to be separated, as they cannot analyze the whole gel. The ROI-GPN has values of the array data with the number of white pixels analyzed by binary mask. The other methods have values in pixel areas, and normalization was performed to realize a comparison. The normalization was made by taking the maximum value measured by each method when applying four methods to identify the overexpression manually (since there is no automatic one) and dividing it by each of the values of its respective method. Thus, the maximum value for all samples is unity. When sorted by the designed concentration value (lowest to highest), the normalized values increase and do not decrease, as did all the previous methods except for the IPBBIS method developed in this work. The increase only occurs if the methods correctly calculate the size of the bands (by area or by intensity). These results indicate that traditional methods can identify the position of proteins. However, they cannot identify a particular protein band nor determine the concentration or molecular weight, and the rest of the new intensity profile plot cannot be related to any of the proteins present in the same sample. The IPBBIS method identified the most minor and most overexpressed GPN protein and even detected the order of overexpression. These results indicate that the IPBBIS method can be used to identify GPN protein overexpression related to IDC and ILC Her2+ breast cancer and can also be applied to identify overexpression of other proteins of biological interest and to detect the progression of cancer stages in different samples from the same patient. In summary, the IPBBIS method applies a binary mask pixel by pixel, choosing the white intensity value and storing it in an array. The array contains multiple maxima and multiple minima. The intensity value of the minima is related to the separation of the number of samples when analyzing a full gel. As the number of targets decreases, this indicates the separation between proteins. When analyzing per sample for proteins in SDS-PAGE gels, the new image profile values of the multiple minima quantify the level of overexpression of proteins present per sample. Current methods for searching for proteins in SDS-PAGE gels perform image profiling, processing techniques, threshold, and brightness changes but require the analyst to select the region of interest. The IPBBIS method automatically identifies the number of samples in the gel and the amount of proteins in a sample. It also detects the level of overexpression based on molecular weight.