The Journal of the Convergence on Culture Technology (문화기술의 융합)

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
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A COVID-19 Chest X-ray Reading Technique based on Deep Learning

딥 러닝 기반 코로나19 흉부 X선 판독 기법

  • 안경희 (서울여자대학교 소프트웨어융합학과) ;
  • 엄성용 (서울여자대학교 소프트웨어융합학과)
  • Received : 2020.10.22
  • Accepted : 2020.11.14
  • Published : 2020.11.30
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Abstract

Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

전 세계적으로 유행하는 코로나19로 인해 많은 사망자가 보고되고 있다. 코로나19의 추가 확산을 막기 위해서는 의심 환자에 대해 신속하고 정확한 영상판독을 한 후, 적절한 조치를 취해야 한다. 이를 위해 본 논문은 환자의 감염 여부를 의료진에게 제공해 영상판독을 보조할 수 있는 딥 러닝 기반 코로나19 흉부 X선 판독 기법을 소개한다. 우선 판독모델을 학습하기 위해서는 충분한 데이터셋이 확보되어야 하는데, 현재 제공하는 코로나19 오픈 데이터셋은 학습의 정확도를 보장하기에 그 영상 데이터 수가 충분하지 않다. 따라서 누적 적대적 생성 신경망(StackGAN++)을 사용해 인공지능 학습 성능을 저하하는 영상 데이터 수적 불균형 문제를 해결하였다. 다음으로 판독모델 개발을 위해 증강된 데이터셋을 사용하여 DenseNet 기반 분류모델 학습을 진행하였다. 해당 분류모델은 정상 흉부 X선과 코로나 19 흉부 X선 영상을 이진 분류하는 모델로, 실제 영상 데이터 일부를 테스트데이터로 사용하여 모델의 성능을 평가하였다. 마지막으로 설명 가능한 인공지능(eXplainable AI, XAI) 중 하나인 Grad-CAM을 사용해 입력 영상의 질환유무를 판단하는 근거를 제시하여 모델의 신뢰성을 확보하였다.

Keywords

References

  1. L. Yao, et al, "Learning to diagnose from scratch by exploiting dependencies among labels", 2017. arXiv preprint arXiv:1710.10501
  2. J. Kim, "An Analysis of the effect of Artificial Intelligence on Human Society", The Journal of the Convergence on Culture Technology, Vol. 5, No. 2, pp. 177-182, 2019. doi.org/10.17703/JCCT.2019.5.2.177
  3. G. Litjens, et al, "A survey on deep learning in medical image analysis", Medical image analysis, Vol. 42, pp. 60-88, 2017. doi.org/10.1016/j.media.2017.07.005.
  4. F. Provost, "Machine learning from imbalanced data sets 101", Proceedings of the AAAI'2000 workshop on imbalanced data sets, AAAI Press, Vol. 68, No. 2000, pp. 1-3, 2000
  5. H. Zhang, et al, "Stackgan++: Realistic image synthesis with stacked generative adversarial networks", IEEE transactions on pattern analysis and machine intelligence, Vol. 41, No. 8, pp. 1947-1962, 2018 https://doi.org/10.1109/TPAMI.2018.2856256
  6. E. Tjoa, C. Guan, "A survey on explainable artificial intelligence (XAI): towards medical XAI", pp. 1-21, 2020. doi.org/10.1109/TNNLS.2020.3027314
  7. I. Goodfellow, et al, "Generative adversarial nets", Advances in neural information processing systems, pp. 2672-2680, 2014
  8. A. Radford, et al, "Unsupervised representation learning with deep convolutional generative adversarial networks", 2015. arXiv preprint arXiv:1511.06434
  9. T. Karras, et al, "Progressive growing of gans for improved quality, stability, and variation", 2017. arXiv preprint arXiv:1710.10196
  10. T. Karras, et al, "A style-based generator architecture for generative adversarial networks", Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4401-4410, 2019
  11. M. Mirza, S. Osindero, "Conditional generative adversarial nets", 2014. arXiv preprint arXiv:1411.1784
  12. P. Isola, et al, "Image-to-image translation with conditional adversarial networks", Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1125-1134, 2017
  13. J. Y. Zhu, et al, "Unpaired image-to-image translation using cycle-consistent adversarial networks", Proceedings of the IEEE international conference on computer vision, pp. 2223-2232, 2017
  14. G. Huang, et al, "Densely connected convolutional networks", Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708, 2017
  15. A. Krizhevsky, et al, "Imagenet classification with deep convolutional neural networks", Communications of the ACM, Vol. 60, No. 6, pp. 84-90, 2017. doi.org/10.1145/3065386
  16. M. Kim, K. H. Kang, "Comparison of Neural Network Techniques for Text Data Analysis", International Journal of Advanced Culture Technology, Vol. 8, No. 2, pp. 231-238, 2020. doi.org/10.17703/IJACT.2020.8.2.231
  17. K. He, et al, "Deep residual learning for image recognition", Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016
  18. P. Rajpurkar, et al, "Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning", 2017. arXiv preprint arXiv:1711.05225
  19. R. R. Selvaraju, et al, "Grad-cam: Visual explanations from deep networks via gradient-based localization", Proceedings of the IEEE international conference on computer vision, pp. 618-626, 2017. doi.org/10.1007/s11263-019-01228-7
  20. B. Zhou, et al, "Learning deep features for discriminative localization", Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2921-2929, 2016