Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Deep Learning Algorithm to Identify Cancer Pictures

딥러닝 기반 암세포 사진 분류 알고리즘

  • Seo, Young-Min (Sejong University, Dept. of Electrical Engineering) ;
  • Han, Jong-Ki (Sejong University, Dept. of Electrical Engineering)
  • 서영민 (세종대학교 전자정보통신공학과) ;
  • 한종기 (세종대학교 전자정보통신공학과)
  • Received : 2018.07.11
  • Accepted : 2018.08.22
  • Published : 2018.09.30
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Abstract

CNN (Convolution Neural Network) is one of the most important techniques to identify the kind of objects in the captured pictures. Whereas the conventional models have been used for low resolution images, the technique to recognize the high resolution images becomes crucial in the field of artificial intelligence. In this paper, we proposed an efficient CNN model based on dilated convolution and thresholding techniques to increase the recognition ratio and to decrease the computational complexity. The simulation results show that the proposed algorithm outperforms the conventional method and the thresholding technique enhances the performance of the proposed model.

본 논문에서는 고해상도 자궁경부암 세포사진을 CNN(Convolution Neural Network)을 통해 효과적으로 인식 및 분류하는 방법을 소개한다. 이때 고려되는 세포의 종류는 Ascus, Inflammation, RCC, Normal 로 네 가지가 있다. 본 논문에서는 먼저 기존의 고해상도 이미지를 분류하는 알고리즘을 소개하고, 이 방법을 이용하여 고해상도 세포사진을 분류하는 과정에서 어떤 정보의 손실이 발생하는지 분석한 후, 이를 해결하기 위한 방법을 제시한다. 이를 위해서 본 논문에서 제안하는 학습 모델에서는 dilated convolution을 이용하여 고해상도 사진의 정보의 손실을 최소한으로 줄임과 동시에 학습속도 빠르게 하는 알고리즘을 제시한다. 또한 이미지 전처리 과정으로 임계치를 사용함으로써 암세포를 판단하는데 혼란을 줄 수 있는 부분을 제거함으로써 인식률을 향상시킨다. 본 논문에서 제시되는 실험 결과를 통해, 제안한 알고리즘이 기존 기술보다 높은 인식률을 제공하는 것을 확인할 수 있었다.

Keywords

References

  1. Andre Esteva1, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau & Sebastian Thrun, "Dermatologist-level classification of skin cancer with deep neural networks", Nature, 542, pp. 115-118, Feb., 2017. [DOI: 10.1038/nature21056]
  2. Fabio Alexandre Spanhol, Luiz S. Oliveira, Caroline Petitjean, "Breast cancer histopathological image classification using Convolutional Neural Networks", 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2560-2567, July, 2016. [DOI: 10.1109/IJCNN.2016.7727519]
  3. Korsuk Sirinukunwattana, Shan E Ahmed Raza, Yee-Wah Tsang, "Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images", IEEE transactions on Medical Imaging, Vol. 35, Issue 5, pp. 1196-1206, May 2016. [DOI: 10.1109/TMI.2016.2525803]
  4. Henschen, D, "IBM's Watson could be healthcare game changer." November, 2013. Retrieved from http://www.informationweek.com/software/information-management/ibms-watson-could-be-healthcare-game-changer/d/d-id/1108608
  5. Dorrier, J, "Exponential medicine: Deep learning AI better than your doctor at finding cancer.", November, 2015. Retrieved from https://singularityhub.com/2015/11/11/exponential-medicine-deep-learning-ai-better-than-your-doctor-at-finding-cancer/#sm.0001ivo53nz3nd33wx01kpv5qtzbs
  6. Alex Krizhevsky, "Convolutional Deep Belief Networks on CIFAR-10", pp. 1-9, 2010, retrieved from https://www.cs.toronto.edu/-kriz/conv-cifar10-aug2010.pdf
  7. Aditya Golatkar, Deepak Anand, Amit Sethi, "Classification of Breast Cancer Histology using Deep Learning", International Conference Image Analysis and Recognition, Springer, pp. 837-844, June, 2018. [DOI https://doi.org/10.1007/978-3-319-93000-8_95]
  8. Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, "Going Deeper with Convolutions", Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1-9, June 2015. [DOI: 10.1109/CVPR.2015.7298594]
  9. Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, Zbigniew Wojna, "Rethinking the Inception Architecture for Computer Vision", Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 2818-2826, 2016. [DOI: 10.1109/CVPR.2016.308]
  10. C Szegedy, S Ioffe, V Vanhoucke, AA Alemi, "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning", Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 4278-4284, 2017.
  11. Kim, Kwang-Baek, "Detection and Recognition of Uterine Cervical Carcinoma Cells in Pap Smear Using Kapur Method and Morphological Features", The Journal of the Korea Information and Communications Society, 11th Issue, No. 10, pp. 1992-1998, October, 2007.
  12. Krizhevsky, Alex, and Geoffrey Hinton, "Learning Multiple Layers of Features from Tiny Images", Technical report, University of Toronto, Vol. 1, No.4, pp.7, April, 2009.
  13. Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille, "Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs", Proceedings of ICLR, pp. 1-14, May, 2015
  14. G Fu, C Liu, R Zhou, T Sun, Q Zhang, "Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network", Remote Sensing, vol. 9, Issue 5, pp.1-21, May, 2017. [doi:10.3390/rs9050498]
  15. Vincent Dumoulin and Francesco Visin, "A guide to convolution arithmetic for deep learning", arXiv 2016, arXiv:1603.07285, pp. 1-31, March 2016.
  16. Wuzhen Shi, Feng Jiang, Debin Zhao, "Single image super-resolution with dilated convolution based multi-scale information learning inception module", 2017 IEEE International Conference on Image Processing (ICIP), pp. 977-981, Sept., 2017. [DOI: 10.1109/ICIP.2017.8296427]
  17. Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, "Pyramid Scene Parsing Network", IEEE Confernce on Computer Vision and Pattern Recognition (CVPR), pp. 6230 - 6239, July 2017. [DOI: 10.1109/CVPR.2017.660]
  18. Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille, "DeepLab: Semantic Image Segmentation with Deep Convolution Nets, Atrous Convolution, and Fully Connected CRFs", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, Issue 4, pp. 834-848, April, 2018. https://doi.org/10.1109/TPAMI.2017.2699184