Convolutional Slicer applied to complexity reduction of COVID-19 chest X-rays. Source code associated to the work "Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-ray Case Study" by David Charte, Iván Sevillano-García, María Jesús Lucena-González, José Luis Martín-Rodríguez, Francisco Charte and Francisco Herrera, published on HAIS 2021.
This code is prepared to be used with the COVIDGR-1.0 cross-validations. slicer.py implements the main model and functionality, whereas slicer-conv.ipynb is a Jupyter notebook that can be used to train and run the model interactively.
Copyright (C) 2021 David Charte, Iván Sevillano-García, María Jesús Lucena-González, José Luis Martín-Rodríguez, Francisco Charte and Francisco Herrera
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