# Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing
## 5 Results and Discussion
### Comparison Results  
In Method B, for the BG class extraction, our analysis shows that the correlation metric is the best option to improve accuracy, and for the tissue classification, the Mahalanobis metric is selected for better performance. Hence, Method A with the correlation metric and Method B with correlation/Mahalanobis metrics are compared to the SVM-based approach. The six test HS images (P008-01, P008-02, P012-01, P012-02, P015-01, and P020-01) are used to evaluate both proposed methodologies against the machine learning approach by a leave-one-patient-out cross-validation. On average, Method A provides an overall accuracy of 67.2 \(\pm\) 11.5%, while Method B achieves 76.1 \(\pm\) 12.4%. These results are lower than the accuracy  
Figure 5: Classification maps for Method A with metrics in Equations (12)–(16). (**a**) Synthetic RGB images with the tumor area identified by a yellow line. (**b**) Method A with the Manhattan metric. (**c**) Method A with the Euclidean metric. (**d**) Method A with the correlation metric. (**e**) Method A with the Mahalanobis metric. (**f**) Method A with the SAM metric. obtained by the SVM-based approach (79.2 \(\pm\) 15.6%). However, as can be seen in Figure 6, the classification performance obtained with other metrics for each independent class presents some improvements. The most relevant result for this particular application is the increment in the TT class sensitivity (Figure 6a) with respect to the SVM-based method. Method A and Method B achieve a median sensitivity of 47.8% and 31.3%, respectively, which represent an increase of 26.2% and 9.7% with respect to the result obtained with the SVM approach (21.6%). In addition, the median sensitivity of the NT class is quite constant among the three approaches, being higher than 97% and reaching 99.7% in Method A. On the contrary, the HT class is penalized in Methods A and B, decreasing the median sensitivity result to 46.5% and 18%, respectively, compared to the SVM performance (92.9%). Nonetheless, in this particular application, the accurate identification and differentiation of the NT and TT classes are more important than the identification of the hypervascularized tissue that can be distinguished by the naked eye, or can be identified with an image processing algorithm based on the morphological properties of the blood vessels. With respect to the specificity results (Figure 6b), all the values for the three proposed methods are quite similar, except for the NT and TT classes, where there are some slight differences among the studied  
Figure 6: Average classification performance of Method A, Method B, and the SVM-based approach by using leave-one-patient-out cross-validation. (**a**) Sensitivity, (**b**) specificity, (**c**) F1-score and (**d**) MCC (normalized). methods. In the NT class, Method A drops its median specificity value to 70.4%, while Method B and SVM scheme reach 88.8% and 87.3%, respectively. The specificity of the TT class is also slightly decreased in the two proposed methods, showing higher interquartile ranges (IQR) than the SVM-based approach, but with median values higher than 92%. Regarding the F1-score results, Figure 6c shows that, in the TT class, Methods A and B improve the median values by 30.7% and 31%, respectively, compared to the result obtained by the SVM-based approach (25.7%). For the NT and BG classes, Method B obtains the best median results of 90.7% and 98.3%. In contrast, the SVM-based approach reaches the best result in the HT class (91.5%), which is consistent with the results obtained for the sensitivity metric. Finally, Figure 6d shows the normalized MCC results, which take into account the unbalanced dataset. In these results, the median values of the TT class are quite similar (\(\sim\)66%) among the three methods, while the median of the NT class in Method B reaches the best result of 90.4%. On the contrary, the median of the HT class is reduced in Methods A and B by \(\sim\)19% and \(\sim\)5%, respectively, with respect to the SVM-based approach.