Prediction of diabetic retinopathy based on a committee of random forests
by Maryam Bahrami; Hedieh Sajedi
International Journal of Intelligent Machines and Robotics (IJIMR), Vol. 1, No. 2, 2018

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.

Online publication date: Wed, 26-Sep-2018

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