# Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples
## 3 Experimental Results
### Quantitative Test Results
#### Qualitative Test Results  
The results of the proposed approach can be visualized as a heat map, which shows the predicted tumor probability map of superpixels, or as a classification map, where the superpixels are assigned to a class using a probability threshold of 50%.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Protocol Superpixel-Based Approach (Results at Image Level)**}} & \multicolumn{3}{c}{**Proposed Superpixel-Based Approach**} & \multicolumn{3}{c}{**Previous CNN-Based Approach ([PERSON] et al., 2017)**} \\  & \multicolumn{3}{c}{**(Results at Image Level)**} & \multicolumn{3}{c}{**(Results at Superpixel Level)**} & \multicolumn{3}{c}{**(Results at Patch Level)**} \\ \hline
**Partition** & **ACC** & **Sensitivity** & **Specificity** & **ACC** & **Sensitivity** & **Specificity** & **ACC** & **Sensitivity** & **Specificity** \\ \hline P1 & 1.00 & 1.00 & 1.00 & 0.91 & 0.91 & 0.90 & 0.93 & 0.91 & 0.96 \\ P2 & 0.94 & 0.83 & 1.00 & 0.79 & 0.50 & 0.97 & 0.89 & 0.99 & 0.83 \\ P3 & 1.00 & 1.  
Figure 14 shows four examples of qualitative results obtained from four tumor images, belonging to P3, P4, P5, and P7, with their respective tumor presence probability. For P3, 61% of the superpixels were classified as tumor, thus the image-scale prediction is tumor. The heat maps for P4 and P7 suggest the presence of tumor with high probability. On the contrary, the heat map for P5 shows a misclassification, where about 46% of the superpixels were classified as tumor, so the image is misclassified, being a false negative prediction.  
Figure 15 shows the results of four example non-tumor images that belong to P3, P4, P5, and P7 with their respective tumor probability results. As can be seen, the heat maps for P3, P4 and P5 show a successful classification of non-tumor samples. For P3 and P4, only a few superpixels were classified as tumor, less than 13% of the image in either case. In the case of P5, the image was flagged as non-tumor, but the number of superpixels classified as tumor is higher, about 36%. Finally, we show an example of a false positive, where 68% of the superpixels were classified as tumor and thus the image was flagged as tumor.  
As mentioned previously, the presence of misannotated superpixels in the dataset could lead in misclassifications. For this reason, we selected the 50% tumor threshold. Although in most situations this strategy provides an accurate diagnosis, there are some limitations. For example, in Figure 14, we can observe how the image belonging to P5 was misclassified as a non-tumor image, while the  
Figure 14: Heat and classification maps of four example tumor images. First row shows the synthetic RGB image of the HS cube, while second row represents the SLIC result. Third row represents the heat map, where red colors represent higher tumor probability. Fourth row shows the classification map, where red, green and white colors indicate tumor, non-tumor and light superpixels, respectively. In the last row, the tumor presence probability of the image is indicated. HS images with classification results with tumor probability >= 50% were considered tumor images, while results <50% were considered non-tumor images.  
number of superpixels classified as tumor was around 46%. On the contrary, the image from P7 in Figure 15 represents a false positive because around 68% of the superpixels were classified as tumor. However, this behavior is only found in a few examples of the dataset.