@article{ART002908022},
author={Younghoon Jung and Daewon Kim},
title={Classification of Diabetic Retinopathy using Mask R-CNN and Random Forest Method},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2022},
volume={27},
number={12},
pages={29-40},
doi={10.9708/jksci.2022.27.12.029}

TY - JOUR
AU - Younghoon Jung
AU - Daewon Kim
TI - Classification of Diabetic Retinopathy using Mask R-CNN and Random Forest Method
JO - Journal of The Korea Society of Computer and Information
PY - 2022
VL - 27
IS - 12
PB - The Korean Society Of Computer And Information
SP - 29
EP - 40
SN - 1598-849X
AB - 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%.
KW - Diabetic Retinopathy;Random Forest Classifier;Mask R-CNN;Data Augmentation
DO - 10.9708/jksci.2022.27.12.029
ER -

Younghoon Jung and Daewon Kim. (2022). Classification of Diabetic Retinopathy using Mask R-CNN and Random Forest Method. Journal of The Korea Society of Computer and Information, 27(12), 29-40.

Younghoon Jung and Daewon Kim. 2022, "Classification of Diabetic Retinopathy using Mask R-CNN and Random Forest Method", Journal of The Korea Society of Computer and Information, vol.27, no.12 pp.29-40. Available from: doi:10.9708/jksci.2022.27.12.029

Younghoon Jung, Daewon Kim "Classification of Diabetic Retinopathy using Mask R-CNN and Random Forest Method" Journal of The Korea Society of Computer and Information 27.12 pp.29-40 (2022) : 29.

Younghoon Jung, Daewon Kim. Classification of Diabetic Retinopathy using Mask R-CNN and Random Forest Method. 2022; 27(12), 29-40. Available from: doi:10.9708/jksci.2022.27.12.029

Younghoon Jung and Daewon Kim. "Classification of Diabetic Retinopathy using Mask R-CNN and Random Forest Method" Journal of The Korea Society of Computer and Information 27, no.12 (2022) : 29-40.doi: 10.9708/jksci.2022.27.12.029

Younghoon Jung; Daewon Kim. Classification of Diabetic Retinopathy using Mask R-CNN and Random Forest Method. Journal of The Korea Society of Computer and Information, 27(12), 29-40. doi: 10.9708/jksci.2022.27.12.029

Younghoon Jung; Daewon Kim. Classification of Diabetic Retinopathy using Mask R-CNN and Random Forest Method. Journal of The Korea Society of Computer and Information. 2022; 27(12) 29-40. doi: 10.9708/jksci.2022.27.12.029

Younghoon Jung, Daewon Kim. Classification of Diabetic Retinopathy using Mask R-CNN and Random Forest Method. 2022; 27(12), 29-40. Available from: doi:10.9708/jksci.2022.27.12.029

Younghoon Jung and Daewon Kim. "Classification of Diabetic Retinopathy using Mask R-CNN and Random Forest Method" Journal of The Korea Society of Computer and Information 27, no.12 (2022) : 29-40.doi: 10.9708/jksci.2022.27.12.029