The following publications are possibly variants of this publication:
- On the development of conjunctival hyperemia computer-assisted diagnosis tools: Influence of feature selection and class imbalance in automatic gradingsMaría Luisa Sánchez Brea, Noelia Barreira-Rodríguez, Noelia Sánchez-Maroño, Antonio Mosquera González, Carlos García-Resúa, María Jesús Giráldez Fernández. artmed, 71:30-42, 2016. [doi]
- On the analysis of feature selection techniques in a conjunctival hyperemia grading frameworkMaría Luisa Sánchez Brea, Noelia Barreira-Rodríguez, Noelia Sánchez-Maroño, Antonio Mosquera González, Carlos García-Resúa, Eva Yebra-Pimentel. esann 2016: [doi]
- Assessment of the repeatability in an automatic methodology for hyperemia grading in the bulbar conjunctivaLuisa Sánchez Brea, Noelia Barreira-Rodríguez, Antonio Mosquera González, Katharine Evans. ijcnn 2017: 1673-1680 [doi]
- On the analysis of local and global features for hyperemia gradingL. Sánchez, Noelia Barreira, N. Sánchez, Antonio Mosquera González, Hugo Pena-Verdeal, Eva Yebra-Pimentel. icmv 2016: [doi]
- Comparing Machine Learning Techniques in a Hyperemia Grading FrameworkL. Sánchez-Brea, Noelia Barreira, Antonio Mosquera González, Hugo Pena-Verdeal, Eva Yebra-Pimentel. icaart 2016: 423-429 [doi]
- Defining the Optimal Region of Interest for Hyperemia Grading in the Bulbar ConjunctivaMaría Luisa Sánchez Brea, Noelia Barreira-Rodríguez, Antonio Mosquera González, Katharine Evans, Hugo Pena-Verdeal. cmmm, 2016, 2016. [doi]
- A Novel Framework for Hyperemia Grading Based on Artificial Neural NetworksLuisa Sánchez, Noelia Barreira, Hugo Pena-Verdeal, Eva Yebra-Pimentel. iwann 2015: 263-275 [doi]