On the development of conjunctival hyperemia computer-assisted diagnosis tools: Influence of feature selection and class imbalance in automatic gradings

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ObjectiveThe sudden increase of blood flow in the bulbar conjunctiva, known as hyperemia, is associated to a red hue of variable intensity. Experts measure hyperemia using levels in a grading scale, a procedure that is subjective, non-repeatable and time consuming, thus creating a need for its automatisation. However, the task is far from straightforward due to data issues such as class imbalance or correlated features. In this paper, we study the specific features of hyperemia and propose various approaches to address these problems in the context of an automatic framework for hyperemia grading.

论文关键词:Image processing,Multi-layer perceptron,Radial basis function network,Random forests,Correlation-based feature selection,Relief,SMOReg,M5,SMOTE,Oversampling,Undersampling,Hyperemia grading

论文评审过程:Received 25 January 2016, Revised 16 June 2016, Accepted 23 June 2016, Available online 30 June 2016, Version of Record 6 July 2016.

论文官网地址:https://doi.org/10.1016/j.artmed.2016.06.004