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KMID : 1137820190400020048
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의공학회지
2019 Volume.40 No. 2 p.48 ~ p.54
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Validation Data Augmentation for Improving the Grading Accuracy of Diabetic Macular Edema using Deep Learning
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Lee Tae-Soo
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Abstract
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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.
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KEYWORD
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deep learning, Validation data augmentation, Diagnostic accuracy, Diabetic macular edema
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