데이터 증강을 이용한 의료 이미지 분류 정확도 개선에 관한 연구
A Study on Improving the Accuracy of Medical Images Classification Using Data Augmentation
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초록
본 연구는 합성곱 신경망 모델에서 이미지 데이터 증강을 통하여 대장암 진단 모델의 정확도를개선하고자 하였다. 이미지 데이터 증강은 기초 이미지 조작 방법을 이용하여 뒤집기, 회전, 이동, 밀림, 주밍을 사용하였다. 본 연구에서는 실험설계를 위해 보유하고 있는 5000개의 이미지 데이터에 대해 훈련 데이터와 평가 데이터로 각각 4000개와 1000개로 나누었으며, 훈련 데이터 4000개에대해 이미지 데이터 증강 기법으로 4000개와 8000개의 이미지를 추가하여 모델을 학습시켰다. 평가 결과는 훈련 데이터 4000개, 8000개, 12000개에 대한 분류 정확도가 각각 85.1%, 87.0%, 90.2% 로 나왔으며 이미지 데이터 증강에 따른 개선 효과를 확인하였다.
Abstract
This paper attempted to improve the accuracy of the colorectal cancer diagnosis model using image data augmentation in convolutional neural network. Image data augmentation was performed by flipping, rotation, translation, shearing and zooming with basic image manipulation method. This study split 4000 training data and 1000 test data for 5000 image data held, the model is learned by adding 4000 and 8000 images by image data augmentation technique to 4000 training data. The evaluation results showed that the clasification accuracy for 4000, 8000, and 12,000 training data were 85.1%, 87.0%, and 90.2%, respectively, and the improvement effect depending on the increase of image data was confirmed.
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