Machine learning classifiers for detection of glaucoma

Reshma Verma, Lakshmi Shrinivasan, Basvaraj Hiremath

Abstract


Glaucoma is a disease that affects the optic nerve. This disease, over a period of time, can lead to loss of vision. Which is known as ‘silent thief of sight’. There are several methods in which the disease can be treated, if detected at an early stage It is not possible for any technology, including artificial intelligence, to replace a doctor. However, it is possible to develop a model based on several classical image processing algorithms, combined with artificial intelligence that can detect onset of glaucoma based on certain parameters of the retinal fundus. This model would play an important role in early detection of the disease and assist the doctor. The traditional methods to detect glaucoma, as efficient as they may be, are usually expensive, a machine learning approach to diagnose from fundus images and accurately classify its severity can be considered to be efficient. Here we propose support vector machine (SVM) method to segregate, train the models using a high-end graphics processor unit (GPU) and augment the hull convex approach to boost the accuracy of the image processing mechanisms along with distinguishing the different stages of glaucoma. A web application for the screening process has also been adopted.

Keywords


Artificial intelligence; Contrast limited adaptive histogram equalization; Convex hul; Cup-disk ratio; Graphics processor unit; Optical coherence tomography; Support vector machine

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DOI: http://doi.org/10.11591/ijai.v12.i2.pp806-814

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