# Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples
## 3 Experimental Results
### Supervised Classification Results
#### 3.2.1 Validation Results
Second, the results obtained with optimized models show high accuracy, sensitivity and specificity for Folds 2 and 4 (higher than 88%), while models for Folds 1 and 3 suffer from low sensitivity (47-54%) and low specificity (35-41%), respectively. However, only Fold 1 shows remarkable PPV and F1 results. Finally, the results suggest the hyperparameter selection using BA as the optimization function provides subtly better  
Figure 9: Number of non-tumor and tumor superpixels per patient (**A**) and percentage of the total per fold (**B**). performance, obtaining an improvement of 1% in the ACC, sensitivity, specificity and PPV metrics and an improvement of 2% in the F1 metric with a reduced standard deviation (std). As mentioned in the dataset description, the usage of a single patient as validation patient may result in overfitting on the validation patient. We ensure there is no overfitting to the validation data by checking for similar performance between the classification of the training and validation samples within a fold. In this work, the dataset annotation was performed at a macroscopic level. Therefore, the HS images annotated as tumor would contain some superpixels with spectral features which are not indicative of tumor, such as superpixels that are common in both tumor and non-tumor tissue. These wrong annotations could lead to false positives in the superpixel classification. For this reason, we considered an alternative approach to evaluate the results. Instead of performing the classification over all the superpixels of the validation patient, we also evaluated the image level. To this end, we used the trained SVM model to classify each image from the validation patient. Then, an image is flagged as tumor if more than 50% of the image superpixels are classified as tumor. On the contrary, if fewer than 50%, then the image is flagged as non-tumor. The threshold value of 50% to decide if an image should be flagged as tumor or not was experimentally determined using the validation data. Although it seems 50% is a high value, since intuitively the presence of a few superpixels flagged as tumor may suggest the presence of tumor, this high threshold can be a consequence of the aforementioned incorrectly annotated superpixels. These inappropriate annotations would lead to false positives in the superpixel classification. A graphic representation of this approach is shown on Figure 10.  
Results on the validation set using the image classification approach are shown in Figure 11. These results were obtained from the SVM model optimized using BA and sensitivity. On the one hand, similarly to the previous evaluation, Folds 2 and 4 present the best performance. In Fold 2, all the images are correctly classified. For Fold 4, only a single non-tumor image and one tumor image are misclassified. For these folds, the performance of both types of SVM models work similarly. On the other hand, the classification performance for Folds 3 and 4 is lower. In Fold 1, the sensitivity is low, and several images from tumor samples are not flagged as tumor, but all the images from non-tumor class are correctly classified. In opposition, in Fold 3 all tumor images are correctly classified, but one half of the non-tumor images are misclassified. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**No Opt. **} & \multicolumn{3}{c}{**Opt. Metric (BA)**} & \multicolumn{3}{c}{**Opt. Metric (Sensitivity)**} \\ \hline
**Partition** & **ACC** & **Sens. ** & **Spec. ** & **PPV** & **F1** & **ACC** & **Sens. ** & **Spec. ** & **PPV** & **F1** & **ACC** & **Sens. ** & **Spec. ** & **PPV** & **F1** \\ \hline Fold 1 & 0.65 & 0.12 & 0.78 & 0.13 & 0.13 & 0.88 & 0.54 & 0.97 & 0.84 & 0.66 & 0.87 & 0.47 & 0.97 & 0.81 & 0.60 \\ Fold 2 & 0.81 & 0.57 & 0.91 & 0.73 & 0.64 &  
Regarding the comparison about the type of optimization for hyperparameter selection, the performance of the SVM optimized by BA is subtly higher compared to optimization driven by sensitivity.