Enfoqué Híbrido para la Clasificación de Muestras Histológicas para la Detección de Tumores Cerebrales: Descomposición lineal y Redes Neuronales - Cruz Guerrero, Inés Alejandro; Campos Delgado, Daniel Ulises; Mejía Rodriguez, Aldo Rodrigo; Ortega, Samuel; Fabelo, Himar; Marrero Callico, Gustavo Iván (Memorias del Congreso Nacional de Ingeniería Biomédica, 2021)
Hyperspectral imaging has demonstrated its potential to provide information of the chemical composition of tissue and also of its morphological characteristics. However, discerning the presence of a pathology through this information is not a simple task. Because of this, a hybrid methodology is proposed in this work, which combines the identification of characteristic components present in a hyperspectral image from linear unmixing methods, and the ability to distinguish patterns from a neural network. The results of this research show that the proposed method can distinguish a tumor condition from histological brain samples with an average accuracy of 86%. The study demonstrates the potential of hybrid classification methodologies in the analysis of spectral information for the identification of histological samples affected by tumor tissue.