마스크 R-CNN과 랜덤 포레스트 방법을 이용한 당뇨병성 망막병증 분류
Classification of Diabetic Retinopathy using Mask R-CNN and Random Forest Method
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참고문헌
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초록
본 논문에서는 딥러닝 기법의 하나인 Mask R-CNN과 랜덤포레스트 분류기를 이용해 당뇨병성 망막병증의 병리학적인 특징을 검출하고 분석하여 자동 진단하는 시스템을 연구하였다. 당뇨병성 망막병증은 특수장비로 촬영한 안저영상을 통해 진단할 수 있는데 밝기, 색조 및 명암은 장치에 따라다를 수 있으며 안과 전문의의 의료적 판단을 도울 인공지능을 이용한 자동진단 시스템 연구와 개발이 가능하다. 이 시스템은 미세혈관류와 망막출혈을 Mask R-CNN 기법으로 검출하고, 후처리 과정을거쳐 랜덤포레스트 분류기를 이용하여 안구의 정상과 비정상 상태를 진단한다. Mask R-CNN 알고리즘의 검출 성능 향상을 위해 이미지 증강 작업을 실시하여 학습을 진행하였으며 검출 정확도 측정을위한 평가지표로는 다이스 유사계수와 Mean Accuracy를 사용하였다. 비교군으로는 Faster R-CNN 기법을 사용하였고 본 연구를 통한 검출 성능은 평균 90%의 다이스 계수를 통한 정확도를 나타내었으며 Mean Accuracy의 경우 91% 정확도의 검출 성능을 보였다. 검출된 병리증상을 토대로 랜덤포레스트 분류기를 학습하여 당뇨병성 망막 병증을 진단한 경우 99%의 정확도를 보였다.
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%.
참고문헌 (20)
- American Diabetes Association, "Standards of Medical Care in Diabetes—2014", Diabetes care 37, ~ (2014)
- H. -Y. Wu, "Diagnostic Performance of Random Urine Samples using Albumin Concentration vs Ratio of Albumin to Creatinine for Microalbuminuria Screening in Patients with Diabetes Mellitus: A Systematic Review and Meta-Analysis", JAMA Internal Medicine, 174(7), pp.1108~1115 (2014)[DOI]
- S. Chaudhary, "Proliferative Diabetic Retinopathy (PDR)", Disease-a-Month, 67(5) (2021)
- Y. Zhou, "A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability", IEEE Transactions on Medical Imaging, 40(3), pp.818~828 (2021)[DOI]
- 윤정석, "무산동 안저 사진기로 진단된 당뇨망막병증 위험 요인 분석: 제5기 국민건강영양조사 자료", 대한안과학회지, 60(6), pp.555~568 (2019), 대한안과학회
- S. J. Park, "Comparative Effectiveness of Antihypertensive Drugs for Preventing Diabetic Retinopathy in Patients with New Onset Type 2 Diabetes", National Evidence-Based Healthcare Collaborating Agency, 18(12), pp.208~217 (2018)
- Padmanayana, "Binary Classification of DR-Diabetic Retinopathy using CNN with Fundus Colour Images", Materials Today: Proceedings, 58, pp.212~216 (2022)[DOI]
- ETDRS, "Grading Diabetic Retinopathy from Stereoscopic Color Fundus Photographs — An Extension of the Modified Airlie House Classification: RN. 10", Ophthalmology, 127(4), pp.S99~S119 (2020)[DOI]
- K. He, "Mask R-CNN", Proceedings of the IEEE International Conference on Computer Vision (2017)
- S. Sooraj, "Automatic Classification of Diabetic Retinopathy Based on Deep Learning - A Review", International Conference on Futuristic Technologies in Control Systems & Renewable Energy (ICFCR), pp.1~5 (2020)
- S. Roychowdhury, "Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification", IEEE Journal of Biomedical and Health Informatics, 19(3), pp.1118~1128 (2014)[DOI]
- V. Gulshan, "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs", Jama, 316(22), pp.2402~2410 (2016)[DOI]
- M. J. J. P. Grinsven, "Fast Convolutional Neural Network Training using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images", IEEE Transactions on Medical Imaging, 35(5), pp.1273~1284 (2016)[DOI]
- L. Qiao, "Diabetic Retinopathy Detection Using Prognosis of Microaneurysm and Early Diagnosis System for Non-Proliferative Diabetic Retinopathy Based on Deep Learning Algorithms", IEEE Access, 8, pp.104292~104302 (2020)[DOI]
- J. Wang, "Simultaneous Diagnosis of Severity and Features of Diabetic Retinopathy in Fundus Photography Using Deep Learning", IEEE Journal of Biomedical and Health Informatics, 24(12), pp.3397~3407 (2020)[DOI]
- K. Shankar, "Hyperparameter Tuning Deep Learning for Diabetic Retinopathy Fundus Image Classification", IEEE Access, 8, pp.118164~118173 (2020)[DOI]
- S. Qummar, "A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection", IEEE Access, 7, pp.150530~150539 (2019)[DOI]
- X. Li, "CANet:Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading", IEEE Transactions on Medical Imaging, 39(5), pp.1483~1493 (2020)[DOI]
- Kaggle Diabetic Retinopathy Detection Competition Report, "B. Graham", University of Warwick, ~ (2015)
- E. Decencière, "Feedback on a Publicly Distributed Image Database: the Messidor Database", Image Analysis and Stereology, 33(3), pp.231~234 (2014)[DOI]