Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
권호 표지
DOI QR코드

DOI QR Code

Classification of Leukemia Disease in Peripheral Blood Cell Images Using Convolutional Neural Network

  • Tran, Thanh (Dept. of IT Convergence and Application Engineering, Pukyong National University) ;
  • Park, Jin-Hyuk (Dept. of IT Convergence and Application Engineering, Pukyong National University) ;
  • Kwon, Oh-Heum (Dept. of IT Convergence and Application Engineering, Pukyong National University) ;
  • Moon, Kwang-Seok (Dept. of Electronics, Pukyong National University) ;
  • Lee, Suk-Hwan (Dept. of Information Security, Tongmyong University) ;
  • Kwon, Ki-Ryong (Dept. of IT Convergence and Application Engineering, Pukyong National University)
  • Received : 2018.07.06
  • Accepted : 2018.09.10
  • Published : 2018.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

Classification is widely used in medical images to categorize patients and non-patients. However, conventional classification requires a complex procedure, including some rigid steps such as pre-processing, segmentation, feature extraction, detection, and classification. In this paper, we propose a novel convolutional neural network (CNN), called LeukemiaNet, to specifically classify two different types of leukemia, including acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), and non-cancerous patients. To extend the limited dataset, a PCA color augmentation process is utilized before images are input into the LeukemiaNet. This augmentation method enhances the accuracy of our proposed CNN architecture from 96.9% to 97.2% for distinguishing ALL, AML, and normal cell images.

Keywords

References

  1. Leukemia: What you need to know. https://www.medicalnewstoday.com/articles/142595.php (accessed Dec., 25, 2017).
  2. Leukemia stages. https://www.cancercenter.com/leukemia/stages/ (accessed Apr., 30, 2018).
  3. C. Raje and J. Rangole, "Detection of Leukemia in Microscopic Images using Image Processing," Proceedings of 2014 International Conference on Communication and Signal Processing, Melmaruvathur, pp. 255-259, 2014.
  4. I. Vincent, K.R. Kwon, S.H. Lee, and K.S. Moon, "Acute Lymphoid Leukemia Classification using Two-Step Neural Network Classifier," Proceedings of 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV), pp. 1-4, 2015.
  5. D. Goutam and S. Sailaja, "Classification of Acute Myelogenous Leukemia in Blood Microscopic Images using Supervised Classifier," Proceedings of 2015 IEEE International Conference on Engineering and Technology (ICETECH ), Coimbatore, pp. 1-5, 2015.
  6. M.C. Su, C.Y. Cheng, and P.C. Wang, "A Neural-Network-Based Approach to White Blood Cell Classification," The Scientific World Journal, Vol. 2014, Article ID 796371, 9 pages, 2014.
  7. S.C. Neoh, W. Srisukkham, L. Zhang, S. Todryk, B. Greystoke, C.P. Lim, et al., "An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images," Scientific Reports, Vol. 5, pp. 1-14, 2015. https://doi.org/10.9734/JSRR/2015/14076
  8. J. Song, H.I. Kim, and Y.M. Ro, "Fast and Robust Face Detection based on CNN in Wild Environment," Journal of Korea Multimedia Society, Vol. 19, No. 8, pp. 1310-1319, 2016. https://doi.org/10.9717/kmms.2016.19.8.1310
  9. S.W. Park and D.Y. Kim, "Performance Comparison of Convolution Neural Network by Weight Initialization and Parameter Update Method1," Journal of Korea Multimedia Society, Vol. 21, No. 4, pp. 441-449, 2018. https://doi.org/10.9717/KMMS.2018.21.4.441
  10. A. Krizhevsky, I. Sutskever, and G.E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," NIPS' 12 Proceedings of the 25th International Conference on Neural Information Processing Systems, Vol. 1, pp. 1097-1105, 2012.
  11. S. Kansal, S. Purwar, and R.K. Tripathi, "Trade-off between Mean Brightness and Contrast in Histogram Equalization Technique for Image Enhancement," 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 195-198, 2017.
  12. Padding and Shearing an Image Simultaneousl. https://www.mathworks.com/help/images/padding-and-shearing-an-image-simultaneously.html (accessed Dec., 24, 2017).
  13. L, Perez and J. Wang, "The Effectiveness of Data Augmentation in Image Classification using Deep Learning,", eprint arXiv:1712.04621, 2017.
  14. E. U. Francis, M. Y. Mashor, R. Hassan, and A. Abdullah, "Screening of Bone Marrow Slide Images for Leukemia Using Multilayer Perceptron (MLP)," 2011 IEEE Symposium on Industrial Electronics and Applications, Langkawi, pp. 643-648, 2011.
  15. L. Putzu, G. Caocci, and C.D. Ruberto, "Leucocyte Classification for Leukaemia Detection using Image Processing Techniques," Artificial Intelligence in Medicine, Vol. 62, No. 3, pp. 179-191, 2014. https://doi.org/10.1016/j.artmed.2014.09.002
  16. M.M. Amin, S. Kermani, A. Talebi, and M.G. Oghli, "Recognition of Acute Lymphoblastic Leukemia Cells in Microscopic Images Using K-Means Clustering and Support Vector Machine Classifier," Journal of medical signals and sensors, Vol. 5, pp. 49-58, 2015.