Automatic Parallel Detection of Neovascularization from Retinal Images Using Ensemble of Extreme Learning Machine.

Abstract

Retinopathy screening is a non-invasive method to collect retinal images and neovascularization detection from retinal images plays a significant role on the identification and classification of diabetes retinopathy. In this paper, an automatic parallel detection framework for neovascularization with color retinal images using ensemble of extreme learning machine is proposed. The framework employs two Map-Reduce Jobs to extract features and trains Extreme Learning Machine models. Ensemble methods such as bagging, subspace partitioning and cross validating are used to increase the accuracy. The framework is evaluated with retinal images from MESSIDOR database. Experimental results show the framework can improve the detection accuracy, as well as speedup the processing time to 22 times on average.