지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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This paper proposed a method of validation data augmentation for improving the grading accuracy of diabetic macular edema (DME) using deep learning. The data augmentation technique is basically applied in order to secure diversity of data by transforming one image to several images through random translation, rotation, scaling and reflection in preparation of input data of the deep neural network (DNN). In this paper, we apply this technique in the validation process of the trained DNN, and improve the grading accuracy by combining the classification results of the augmented images. To verify the effectiveness, 1,200 retinal images of Messidor dataset was divided into training and validation data at the ratio 7:3. By applying random augmentation to 359 validation data, 1.61 ± 0.55% accuracy improvement was achieved in the case of six times augmentation (N=6). This simple method has shown that the accuracy can be improved in the N range from 2 to 6 with the correlation coefficient of 0.5667. Therefore, it is expected to help improve the diagnostic accuracy of DME with the grading information provided by the proposed DNN.
목차
등록된 정보가 없습니다.
참고문헌 (14)
참고문헌 신청
계효정 / 2015 / 당뇨망막병증 최신 지견 / 대한내과학회지 89(3):271~276
Jee D / 2013 / Prevalence and risk factors for diabetic retinopathy : the Korea National Health and Nutrition Examination Survey 2008-2011 / Invest Ophthalmol Vis Sci 54(10):6827~6833
Abramoff MD / 2016 / Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning / Invest Ophthalmol Vis Sci 57(13):5200~5206
Abramoff MD / 2018 / Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices / npj Digital Medicine 1(39):1~8
van der Heijden AA / 2018 / Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System / Acta Ophthalmol 96(1):63~68
Jee D / 2013 / Prevalence and risk factors for diabetic retinopathy : the Korea National Health and Nutrition Examination Survey 2008-2011 / Invest Ophthalmol Vis Sci 54(10):6827~6833
Abramoff MD / 2016 / Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning / Invest Ophthalmol Vis Sci 57(13):5200~5206
Abramoff MD / 2018 / Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices / npj Digital Medicine 1(39):1~8
van der Heijden AA / 2018 / Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System / Acta Ophthalmol 96(1):63~68
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