JIPS (한국정보처리학회)
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| 한글제목(Korean Title) |
Feature Extraction of Non-proliferative Diabetic Retinopathy Using Faster R-CNN and Automatic Severity Classification System Using Random Forest Method |
| 영문제목(English Title) |
Feature Extraction of Non-proliferative Diabetic Retinopathy Using Faster R-CNN and Automatic Severity Classification System Using Random Forest Method |
| 저자(Author) |
Younghoon Jung
Daewon Kim
|
| 원문수록처(Citation) |
VOL 18 NO. 5 PP. 0599 ~ 0613 (2022. 10) |
한글내용
(Korean Abstract) |
|
영문내용
(English 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. |
| 키워드(Keyword) |
Faster R-CNN
Classification
Machine Learning
Non-proliferative Diabetic Retinopathy
Random Forest
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| 파일첨부 |
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