Feature Extraction of Non-proliferative Diabetic Retinopathy Using Faster R-CNN and Automatic Severity Classification System Using Random Forest Method


Younghoon Jung, Daewon Kim, Journal of Information Processing Systems Vol. 18, No. 5, pp. 599-613, Oct. 2022  

10.3745/JIPS.04.0252
Keywords: Faster R-CNN, Classification, Machine Learning, Non-proliferative Diabetic Retinopathy, Random Forest
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Abstract

Non-proliferative diabetic retinopathy is a representative complication of diabetic patients and is known to be a major cause of impaired vision and blindness. There has been ongoing research on automatic detection of diabetic retinopathy, however, there is also a growing need for research on an automatic severity classification system. This study proposes an automatic detection system for pathological symptoms of diabetic retinopathy such as microaneurysms, retinal hemorrhage, and hard exudate by applying the Faster R-CNN technique. An automatic severity classification system was devised by training and testing a Random Forest classifier based on the data obtained through preprocessing of detected features. An experiment of classifying 228 test fundus images with the proposed classification system showed 97.8% accuracy.


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Cite this article
[APA Style]
Younghoon Jung and Daewon Kim (2022). Feature Extraction of Non-proliferative Diabetic Retinopathy Using Faster R-CNN and Automatic Severity Classification System Using Random Forest Method. Journal of Information Processing Systems, 18(5), 599-613. DOI: 10.3745/JIPS.04.0252.

[IEEE Style]
Y. Jung and D. Kim, "Feature Extraction of Non-proliferative Diabetic Retinopathy Using Faster R-CNN and Automatic Severity Classification System Using Random Forest Method," Journal of Information Processing Systems, vol. 18, no. 5, pp. 599-613, 2022. DOI: 10.3745/JIPS.04.0252.

[ACM Style]
Younghoon Jung and Daewon Kim. 2022. Feature Extraction of Non-proliferative Diabetic Retinopathy Using Faster R-CNN and Automatic Severity Classification System Using Random Forest Method. Journal of Information Processing Systems, 18, 5, (2022), 599-613. DOI: 10.3745/JIPS.04.0252.