# Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples
## 3 Experimental Results
### Quantitative Test Results  
In this section, we show the classification results for the test set of each fold. The quantitative results obtained for the test set for the SVM Linear classifier optimized with the BA and the sensitivity metrics are shown in Figure 12. On average, the use of the BA metric for the hyperparameter optimization provided better ACC results (\(82.6\pm 7.1\%\)) than the Sensitivity metric (\(78.3\pm 6.2\%\)). Considering  
Figure 10: Classification results of the majority voting of superpixels classified by SVM. The top row shows a representative non-tumor image RGB composite and its corresponding SLIC result (left), the corresponding superpixel heat map and the binarized form of tumor and non-tumor superpixels (right), where the white color represent the superpixels that correspond with light, green and red colors represent the non-tumor and tumor superpixels, respectively. The result contains less than 50% tumor superpixels, so it is classified as non-tumor. The bottom row shows of a tumor image, where it is possible to observe that the result contains higher than 50% tumor superpixels, so this image is classified as tumor. Figure 11: Average image classification results of each fold in the validation set using both metrics (BA and Sensitivity) for the hyperparameter optimization of the SVM Linear kernel. sensitivity and specificity results, the classifier optimized with the BA metric provided a result of \(88.5\pm 10.2\%\) and \(80.2\pm 16.9\%\), respectively, representing an improvement in the sensitivity of 4.3% with respect to the classifier optimized with the sensitivity metric. Regarding the differences between the use of BA metric and sensitivity metric in the PPV and F1 results, PPV were practically the same, achieving \(73.4\pm 19\%\) and \(73.1\pm 17.6\%\), respectively, while F1 improved 1.6% using the BA metric (\(78.3\pm 6.4\%\)). For this reason, the BA metric was shown as a suitable evaluation metric to optimize the hyperparameters of a supervised classifier with imbalanced datasets for this specific application. Finally, in Figure 13, we show the results obtained of each patient individually. For every patient except for P2, P6, P8 and P10, the sensitivity is higher than 90%. For P2 and P8, the sensitivity is also high, beyond 80%. For P6 and P10, the sensitivity is low. These patients were classified using models from Folds 2 and 4, respectively. However, models from those folds demonstrated good sensitivity for the remaining patients. This fact will be further investigated in future works. The specificity can only be measured for patients with both types of annotations (non-tumor and tumor), i.e., from P1 to P8. Five of these patients present a sensitivity of 100%, while the others present low specificity. The worst case is P7, where the specificity is 10%. The high sensitivity of the proposed methodology suggests that it could serve as a clinical tool to detect images with suspicious tumor presence for a pathologist to examine. However, despite the fact that the overall amount of time spent in examination could be reduced, the low specificity would likely result in some false positives. Table 2 shows the results of the proposed superpixel approach (reporting both the results at image and superpixel level) and the previous work, where the same data were classified using a patch-based CNN approach (reporting the results at patch level) [10]. As shown in Table 2, data from P6 are missing from the CNN experiments. In our previous work, we found an error in the annotations of P6, and this patient was removed from the dataset. In this study, we corrected the annotation errors of this patient. However, to provide a fair comparison between the two different processing approaches, P6 patient has not been included in the computation of the mean metrics. The average ACC results obtained with the proposed approach (considering the results at image level) is 3% below the CNN-based approach.