Effect of multi filters in glucoma detection using random forest classifier

Abstract

Glaucoma is an eye disease that damages the optic nerve which connects the eye to the brain. When the fluid pressure inside the eye (intraocular pressure) increases, the optic nerve get impaired and has doubled the chance for diabetic patients resulting in irreversible loss of vision if not detected in early stages. In developing countries, due to the scarcity of ophthalmic experts and lab facilities, the needs for eye disease detecting automation system are increased without saying. The field of artificial intelligence is providing many solution's especially in health care domain. The proposed work generate models for recognizing the presence of glaucoma based on open access public dataset of retinal fundus images using machine learning algorithms with the help of image feature descriptors. It classifies the given retinal fundus image as normal or abnormal in two stages. Firstly it extracts image features using appropriate filters and then it is trained through tree based ensemble classifier to classify the given input image and then the same is tested to get the better accuracy performance. The above two steps are iterated by varying over the three effective filters like edge histogram, fuzzy color and texture histogram and pyramid histogram of gradients. The proposed experiment based on this approach reveals that the use of Edge histogram filter in combination with fuzzy color and texture histogram with Random forest classifier yields maximum accuracy of 80.43% and AUC 0.884. The results obtained by applying multi filters is better than that obtained by applying single filter.