Unifying heterogeneous hyperspectral databases for in vivo human brain cancer classification: Towards robust algorithm development
Permanent lenke
https://hdl.handle.net/10037/36678Dato
2025-02-18Type
Journal articleTidsskriftartikkel
Peer reviewed
Forfatter
Martín-Pérez, Alberto; Martinez-Vega, Beatriz; Villa, Manuel; Leon, Raquel; Martinez de Ternero, Alejandro; Fabelo, Himar; Ortega, Samuel; Quevedo, Eduardo; Callico, Gustavo M.; Juarez, Eduardo; Sanz, CésarSammendrag
Methods - This study evaluated conventional and deep learning methods (KNN, RF, SVM, 1D-DNN, 2D-CNN, Fast 3D-CNN, and a DRNN), and advanced classification frameworks (LIBRA and HELICoiD) using cross-validation on 16 and 26 patients from each database, respectively.
Results - For individual datasets,LIBRA achieved the highest sensitivity for tumor classification, with values of 38 %, 72 %, and 80 % on the SLIMBRAIN, HELICoiD (20 bands), and HELICoiD (128 bands) datasets, respectively. The HELICoiD framework yielded the best F1 Scores for tumor tissue, with values of 11 %, 45 %, and 53 % for the same datasets. For the Unified dataset, LIBRA obtained the best results identifying the tumor, with a 40 % of sensitivity and a 30 % of F1 Score.