A novel framework for hyperemia grading based on artificial neural networks

Abstract

A common symptom of several pathologies is hyperemia, that occurs when a certain tissue has an abnormal hue of red. An increase of blood flow causes the engorgement of blood vessels, which produces the coloration. Hyperemia is an important parameter that specialists take into account when diagnosing diseases such as dry eye syndrome or problems derived from contact lenses wearing. In this work, we propose an automatic methodology to measure the hyperemia level of the bulbar conjunctiva. This methodology emphasizes the transformation from the extracted features to grading scales, using artificial neural networks for the process.