Name
Title | ||
|---|---|---|
On the development of conjunctival hyperemia computer-assisted diagnosis tools: Influence of feature selection and class imbalance in automatic gradings. |
Abstract | ||
|---|---|---|
Machine learning methods are able to perform an objective assessment of hyperemia grading, removing both intra- and inter-expert subjectivity while providing a gain in computation time. SMOTE and oversampling approaches minimise the class imbalance problem, while feature selection reduces the number of features from 25 to 3-5 without worsening the MSE. As the differences between the system and a human expert are similar to the differences between experts, we can therefore conclude that the system behaves like an expert. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.artmed.2016.06.004 | Artificial Intelligence in Medicine |
Keywords | DocType | Volume |
ROI,CFS,MLP,RBFN,RF,MSE,ANNs | Journal | 71 |
ISSN | Citations | PageRank |
0933-3657 | 1 | 0.36 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| María Luisa Sánchez Brea | 1 | 1 | 0.70 |
| Noelia Barreira-Rodríguez | 2 | 1 | 1.38 |
| Noelia Sánchez-Maroño | 3 | 406 | 25.39 |
| Antonio Mosquera González | 4 | 115 | 16.72 |
| Carlos García-Resúa | 5 | 2 | 1.40 |
| María Jesús Giráldez Fernández | 6 | 1 | 0.36 |