Title: Prediction of diabetic retinopathy based on a committee of random forests
Authors: Maryam Bahrami; Hedieh Sajedi
Addresses: Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran ' Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
Abstract: Diabetic retinopathy (DR) is an ocular disease generated by complications of diabetes, and it must be discovered quickly for effective cure. By early diagnosis of retinal fundus disease, ophthalmologists can cure the disease or reduce its deterioration, thereby preventing the patients from vision loss. Using enlarged images, ophthalmologists can diagnose DR. In this paper, committee of random forests (CRF) for detection of DR is proposed. In this approach, we use k-means clustering algorithm and random forest classification method to create a new classifier. CRF has been tested on the DR Debrecen dataset, in which 94.76% accuracy is reached in disease or no disease setting.
Keywords: diabetic retinopathy; machine learning; hybrid method; k-means clustering; random forest classification.
International Journal of Intelligent Machines and Robotics, 2018 Vol.1 No.2, pp.133 - 139
Received: 27 Dec 2017
Accepted: 29 Jan 2018
Published online: 26 Sep 2018 *