Feature Extraction of Non-proliferative Diabetic Retinopathy Using Faster R-CNN and Automatic Severity Classification System Using Random Forest Method
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초록
Non-proliferative diabetic retinopathy is a representative complication of diabetic patients and is known to bea major cause of impaired vision and blindness. There has been ongoing research on automatic detection ofdiabetic retinopathy, however, there is also a growing need for research on an automatic severity classificationsystem. This study proposes an automatic detection system for pathological symptoms of diabetic retinopathysuch as microaneurysms, retinal hemorrhage, and hard exudate by applying the Faster R-CNN technique. Anautomatic severity classification system was devised by training and testing a Random Forest classifier basedon the data obtained through preprocessing of detected features. An experiment of classifying 228 test fundusimages with the proposed classification system showed 97.8% accuracy.
Abstract
Non-proliferative diabetic retinopathy is a representative complication of diabetic patients and is known to bea major cause of impaired vision and blindness. There has been ongoing research on automatic detection ofdiabetic retinopathy, however, there is also a growing need for research on an automatic severity classificationsystem. This study proposes an automatic detection system for pathological symptoms of diabetic retinopathysuch as microaneurysms, retinal hemorrhage, and hard exudate by applying the Faster R-CNN technique. Anautomatic severity classification system was devised by training and testing a Random Forest classifier basedon the data obtained through preprocessing of detected features. An experiment of classifying 228 test fundusimages with the proposed classification system showed 97.8% accuracy.
참고문헌 (24)
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