GFAP classification

Published: 7 April 2017| Version 1 | DOI: 10.17632/3jz5zwnmmr.1
Aurora Campo


Matlab application for classification of glioma tumours based on GFAP immunostaining of histological samples.


Steps to reproduce

You must load a table to be classified in substitution to the "prediction" table loaded in the classifier by default. To create your own prediction table, you must run the GFAP segmentation first to extract the features and then create the indexes using the subset size as reference area. NOTE: Training performed on canine glioma tissue.