# Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing
## 5 Results and Discussion
### Comparison Results
The qualitative results represented as classification maps are shown in Figure 7 for Methods A and B and the SVM-based approach. These maps allow evaluating the results obtained in the classification of the all the HS images, including the non-labeled pixels. Figure 7a shows the synthetic RGB images with the tumor area marked by a yellow line, while Figure 7b-d presents the classification maps obtained with the SVM-based approach, Method A and Method B, respectively. In these classification results, the proposed methods improve the labeling of the pixels in the tumor area with respect to the SVM-based approach. However, the proposed methodologies present more false positives in the non-tumor areas. Regarding the other tissue classes, the qualitative results are quite similar, except for the BG class, where Method B shows an accurate identification of the parenchymal area (exposed brain surface) in images P008-01, P008-02, and P012-02. Moreover, by these results, we observe that the low classification performance found in the quantitative results of the HT class in Method A (see Figure 6) is due to the misclassifications between the BG and HT classes in images P008-02 and P015-01, where the main blood vessels are identified as background. This phenomenon does not occur in Method B, where, in general, the hypervascularized areas are well identified. Regarding the comparison of the execution time among the three methods, Figure 8 shows the average time obtained for the six test HS images. In order to better compare the results, a logarithmic scale is used. This execution time-cost involves both the training and classification for the SVM-based approach and the complete execution of both proposed methods in Figures 2 and 3. These results were computed by using MATLAB(r) on an Intel i7-4790K with a working frequency of 4.00  GHz and a RAM memory of 8 GB. The SVM-based approach requires almost four hours to train and perform a classification of one HS image. However, the proposed methods based on the EbEAE algorithm only require an average of \(\sim\)30 s to train and classify the datasets, achieving similar accuracy results as discussed in the previous paragraphs. In summary, the proposed Methods A and B offer speedup factors of \(\sim\)459\(\times\) and \(\sim\)429\(\times\), respectively, compared to the SVM-based approach. Figure 8: Execution time for Methods A and B and the SVM-based approach (represented in logarithmic scale). Figure 7: Classification map comparison of Method A, Method B, and the SVM-based approach. (**a**) Synthetic RGB images with the tumor area identified by a yellow line. (**b**) SVM-based approach. (**c**) Method A with the correlation metric. (**d**) Method B with correlation/Mahalanobis metrics. The results obtained in this preliminary study were compared with other works that employed the same database for classification purposes. In [27], five in vivo brain surface HS images with confirmed grade IV glioblastoma tumor were used for brain cancer detection. The SVM algorithm was employed using an intra-patient methodology for evaluating the supervised classifier. This research obtained specificity and sensitivity results higher than 99% for the TT class. However, due to the intra-patient methodology, where the data from the same patient were employed for both training and test, the results were highly optimistic, without taking into account the intra-patient variability of the spectral data. In [28], the authors presented the results obtained with different deep-learning techniques using the in vivo human brain cancer HS dataset employed in this work and evaluated eight HS images using a leave-one-patient-out cross-validation methodology. The sensitivity obtained for the NT and TT classes was 90% and 42%, respectively. Furthermore, the deep-learning results were compared with a linear SVM-based classifier that obtained a sensitivity for the NT and TT classes of 95% and 26%, respectively.