# Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing
## 6 Conclusions  
In this work, we proposed two methods based on a BLU algorithm (EBEAE) to classify intraoperative HS images of in vivo brain tissue, and we compared the results with a machine learning classification method based on a supervised SVM strategy. The main contribution of this paper is achieving a competitive classification performance with respect to the SVM strategy at the lowest computational time cost. One important feature of our proposal is that both Methods A and B require roughly the same computational cost, but we observe, as the advantages of Method B, less variability in the classification performance. Furthermore, as originally intended, Method B is more accurate in identifying the BG class (F1-score and MCC metric), and both Methods A and B improve the sensitivity and F1-score in detecting the TT class compared to the SVM-based approach. In this study, we were able to achieve speedup factors of ~459\(\times\) and ~429\(\times\) by using the proposed methods with respect to the SVM-based approach, while keeping constant and even slightly improving the classification metrics. Note that in Method A, the training stage by using the ground-truth datasets is equivalent to extracting the characteristic end-members per class, and in Method B, we add the segmentation of the background class. Meanwhile, the labeling of the pixels in the HS images is equivalent to computing the minimum distance to the classes' sets. Both processes are significantly less complex than the training and labeling steps of the SVM-based classifier. Meanwhile, the accuracy results could not be significantly improved due to the intrinsic variability in the intraoperative HS images. For this particular application, reducing the computational time could allow using the spectral data from the current patient to obtain personalized classification results. This particular information could be combined with previous patient datasets to develop a classification model that takes into account inter-patient and intra-patient spectral variability. Moreover, the massive parallelization capabilities of the proposed BLU methods, together with the use of the so-called snapshot HS cameras (cameras that are able to capture spectral and spatial information in one single shot) that provide real-time HSI, could achieve classification results in real time during clinical procedures, improving the outcomes of the surgery and, hence, the patient outcomes and quality of life. One of the main limitations of this preliminary study is the reduced number of patients included in the HS brain cancer database for a better generalization of the classification results. This lack of data was produced due to the challenges of obtaining good-quality HS images during surgical procedures. For this reason, in future works, we will include more patients in the HS database in order to further validate the proposed methodology. Furthermore, the inclusion of morphological post-processing methods will be explored to reduce the misclassifications found in the qualitative classification results. Especially the false positives of the tumor class could be reduced if the method is combined with spatial filtering algorithms. Moreover, further research will be carried out to reduce the processing time of the proposed methodologies by using specific hardware accelerators, such as GPUs (Graphic Processing Units) or FPGAs (field-programmable gate arrays), exploiting the parallelism of these platforms. As stated before, the algorithm acceleration will assist neurosurgeons to identify the tumor area and its boundaries during the surgical procedure, reaching real-time performance. Conceptualization, D.U.C.-D., H.F., S.O. and G.M.C. ; methodology, I.A.C.-G., D.U.C.-D., H.F., S.O. and G.M.C. ; software, I.A.C.-G. and R.L. ; validation, I.A.C.-G., H.F., S.O. and R.L. ; formal analysis, I.A.C.-G. and D.U.C.-D.; investigation, H.F., S.O. and G.M.C. ; resources, H.F., S.O. and G.M.C. ; data curation, H.F. and S.O. ; writing, original draft preparation, I.A.C.-G., D.U.C.-D., H.F., S.O., R.L.