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http://dx.doi.org/10.9708/jksci.2022.27.12.029

Classification of Diabetic Retinopathy using Mask R-CNN and Random Forest Method  

Jung, Younghoon (Graduate School of Computers, Dankook University)
Kim, Daewon (Department of Computer Engineering, Dankook University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.12, 2022 , pp. 29-40 More about this Journal
Abstract
In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.
Keywords
Diabetic Retinopathy; Random Forest Classifier; Mask R-CNN; Data Augmentation; Image Processing;
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