Implementing an Application that Automates Glaucoma Detection Through the Use of Image Processing and Fuzzy Logic

This article centers in providing a system for the optimization of recognizing different structures that constitutes the depth of the retina (retinal fundus images) to build a Glaucoma detection tool. This is achieved through the implementation of an application that uses the OpenCV library to process images and fuzzy logic to deliver an accurate diagnosis and hence control the evolution of the disease. When developing the app, several processing images techniques were implemented which will provide the information we need to determine the state of the disease and the fuzzy logic will aid in interpreting the obtained data. In this article, the combination of two different Glaucoma detection techniques is tested as well as the generation of a disease diagnosis, which offers results within a pre-established range. With the implementation of these techniques, it is stated that a higher refinement is necessary in the detection of Glaucoma starting from the image processing stage.