Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

Development of ResNet-based WBC Classification Algorithm Using Super-pixel Image Segmentation

  • Lee, Kyu-Man (Department of Conversing Technology, Graduate School of Venture, Hoseo University) ;
  • Kang, Soon-Ah (Department of Conversing Technology, Graduate School of Venture and Institute of Health Industry, Hoseo University)
  • Received : 2018.03.27
  • Accepted : 2018.04.11
  • Published : 2018.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose an efficient WBC 14-Diff classification which performs using the WBC-ResNet-152, a type of CNN model. The main point of view is to use Super-pixel for the segmentation of the image of WBC, and to use ResNet for the classification of WBC. A total of 136,164 blood image samples (224x224) were grouped for image segmentation, training, training verification, and final test performance analysis. Image segmentation using super-pixels have different number of images for each classes, so weighted average was applied and therefore image segmentation error was low at 7.23%. Using the training data-set for training 50 times, and using soft-max classifier, TPR average of 80.3% for the training set of 8,827 images was achieved. Based on this, using verification data-set of 21,437 images, 14-Diff classification TPR average of normal WBCs were at 93.4% and TPR average of abnormal WBCs were at 83.3%. The result and methodology of this research demonstrates the usefulness of artificial intelligence technology in the blood cell image classification field. WBC-ResNet-152 based morphology approach is shown to be meaningful and worthwhile method. And based on stored medical data, in-depth diagnosis and early detection of curable diseases is expected to improve the quality of treatment.

Keywords

References

  1. I. Goodfellow, Y. Bengio, and A. Courville, "Deep Learning", MIT Press, 2016.
  2. Pattern Recognition, Pattern Analysis, http://www.ktword.co.kr/abbr_view.phpfim_temp1
  3. Shlee, cgyoo, iklee, and cjhwang, "White blood cell image Retrieving & Clustering System", The Korean Institute of Information Scientists and Engineers, Vol. 26 No. 2, pp. 530-532, Oct. 1999.
  4. K. Simonyan, and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," Vol. 6, pp. 1-14, Apr. 2015
  5. M. D. Zeiler, and R. Fergus, "Visualizing and understanding convolutional networks," In European conference on computer vision, pp. 818-833, Nov. 2014.
  6. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks", NIPS, Vol. 1, pp. 1097-1105, Dec. 2012.
  7. S. Ioffe, and C. Szegedy, "Batch Normalization: Acceler ating Deep Network Training by Reducing Internal Covariate Shift", NIPS, Vol. 3, pp. 1-11, Mar. 2015.
  8. Y. Tang, "Deep Learning using Linear Support Vector Machines", ICML 2013, Vol. 4, Feb. 2015.
  9. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, Dec, 2015.
  10. K. He, X. Zhang, S. Ren, and J. Sun, "Identity Mappings in Deep Residual Networks" Computer Vision and Pattern Recognition, Vol. 3, pp. 1-15, Jul. 2016.
  11. Jmchoi, and wslee, "Development of a Poikilocyte Measuring Method Using Image Analysis Software", Lab Med Online, Vol. 3, No. 1, pp. 6-14, Jan. 2013. https://doi.org/10.3343/lmo.2013.3.1.6
  12. M. Habibzadeh, A. Krzyzak, and T. Fevens, "Comparati ve study of shape, intensity and texture features and support vector machine for white blood cell classification", Journal of Theoretical and Applied Computer Science Vol. 7, No. 1, pp. 20-35, Jan, 2013.
  13. Y. M. Alomari, S. N. H. S. Abdullah, R. Z. Azma, and K. Omar, "Computational and Mathematical Methods in Medicine", pp. 1-17, Apr, 2014.
  14. N. Ghane, A. Vard, A. Talebi, and P. Nematollahy, "Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm", J Med Signals Sens, Vol. 7, No. 2, pp. 92-101. Jun. 2017.
  15. M. I. Razzak, and SaeedaNaz, "Microscopic Blood Smear Segmentation and Classification using Deep Contour Aware CNN and Extreme Machine Learning", Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 49-55, Jul, 2017.
  16. R. Sorgedrager, "Automated malaria diagnosis using convolutional neural networks in an on-field setting", MA, Delft University of Technology, Jan, 2018.
  17. M. Xu, S. Z. Abidi, M. Dao, H. Zhao, and G. E. Karniadakis, "A deep convolutional neural network for classification of red blood cells in sickle cell anemia" PLoS Comput Biol, Vol. 13, No. 10, pp. 1-27, Oct, 2017.
  18. Jhhyun, hylim, and dskang, "A Study on Tracking -Learning-Detection Algorithm and Learning Algorithm for Object Detection System Development", Journal of KIIT. Vol.15, No.12, pp.139-145, Dec. 2017.
  19. H. N. Mhaskar, S. V. Pereverzyev, and M. D. van der Walt, "A Deep Learning Approach to Diabetic Blood Glucose Prediction", Frontiers in Applied Mathematics and Statistics, Vol. 3, No. 14, pp. 1-11, Jul. 2017.
  20. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Neural Information Processing Systems (NIPS), pp. 1097-1105, Feb. 2012.
  21. Jsyoo, and kclee, "Deep learning based image recognition technology trend", Information and Society, pp. 17-24, Jul. 2017.
  22. Identity Mappings in Deep Residual Networks Review, https://kangbk0120.github.io/articles/
  23. A. R, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, "SLIC Superpixels Compared to State-of-the-Art Superpixel Methods". IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 34, No. 11, pp. 2274-2282, Nov. 2012. https://doi.org/10.1109/TPAMI.2012.120
  24. M. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, "Entropy rate superpixel segmentation", IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1-14, Jan. 2014.