Publicación (Artículos en revistas científicas)

Unifying heterogeneous hyperspectral databases for in vivo human brain cancer classification Towards robust algorithm developmen

Martín Pérez, Alberto; Martínez-Vega, Beatriz; Villa Romero, Manuel; León, Raquel; Martínez de Ternero Ruíz, Alejandro; Fabelo, Himar; Ortega, Samuel; Quevedo, Eduardo; Callico, Gustavo M.; Juárez Martínez, Eduardo; Sanz Álvaro, César

Resumen:

Cancer is one of the leading causes of death worldwide, and early and accurate detection is crucial to improve patient outcomes. Differentiating between healthy and diseased brain tissue during surgery is particularly challenging. Hyperspectral imaging, combined with machine and deep learning algorithms, has shown promise for detecting brain cancer in vivo. The present study is distinguished by an analysis and comparison of the performance of various algorithms, with the objective of evaluating their efficacy in unifying hyperspectral databases obtained from different cameras. These databases include data collected from various hospitals using different hyperspectral instruments, which vary in spectral ranges, spatial and spectral resolution, as well as illumination conditions. The primary aim is to assess the performance of models that respond to the limited availability of in vivo human brain hyperspectral data. The classification of healthy tissue, tumors and blood vessels is achieved through the utilisation of different algorithms in two databases: HELICoiD and SLIMBRAIN.

Áreas de investigación:

• Tecnologías de Vídeo e Imagen

Año:

2025

Tipo de publicación:

Artículos en revistas científicas

Revista:

ScienceDirect

Volumen:

7

Páginas:

100183

Mes:

Febrero

DOI:

10.1016/j.cmpbup.2025.100183