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Acute Leukemia Classification Using Sequential Neural Network Classifier in Clinical Decision Support System

  • Ivan Vincent (Dept. of IT Convergence and Applications Eng., Pukyong National University) ;
  • Thanh.T.T.P (Dept. of IT Convergence and Applications Eng., Pukyong National University) ;
  • Suk-Hwan Lee (Dept. of Information Security, Tongmyong University) ;
  • Ki-Ryong Kwon (Dept. of IT Convergence and Applications Eng., Pukyong National University)
  • Received : 2024.09.05
  • Published : 2024.09.30

Abstract

Leukemia induced death has been listed in the top ten most dangerous mortality basis for human being. Some of the reason is due to slow decision-making process which caused suitable medical treatment cannot be applied on time. Therefore, good clinical decision support for acute leukemia type classification has become a necessity. In this paper, the author proposed a novel approach to perform acute leukemia type classification using sequential neural network classifier. Our experimental result only covers the first classification process which shows an excellent performance in differentiating normal and abnormal cells. Further development is needed to prove the effectiveness of second neural network classifier.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (No. 2016R1D1A3B03931003, No. 2017R1A2B2012456) and MSIP (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2017-2016-0-00318) supervised by the IITP(Institute for Information & communications Technology Promotion)".

References

  1. V. F. R.D.Labati, "ALL-IDB: The Acute Lymphoblastic Leukemia Image Database for Image Processing," 18th IEEE International Conference on Image Processing, 2011.
  2. H. A. R.Soltanzadeh, "Extraction of Nucleolus Candidate Zone in White Blood Cells of Peripheral Blood Smear Images Using Curvelet Transform," Hindawi Publishing Corporation, Computational and Mathematical Methods in Medicine, vol. 2012, p. 12, February 2012.
  3. M. A. N. H. N.H. Harun, "Automated Classification of Blast in Acute Leukemia Blood Samples using HMLP Network," Proceedings of the 3rd International Conference on Computing and Informatics, pp. 55-60, June 2011.
  4. S. A. C. M.Madhukar, "New Decision Support Tool for Acute Lymphoblastic Leukemia Classification," Proceeding of SPIE-IS&T Electronic Imaging, SPIE, vol. 8295, 2012.
  5. E. Berner, "Clinical Decision Support Systems: State of the Art," Agency for Healthcare Research and Quality Publication, June 2009.
  6. D. S. K. A.Z. Chitade, "Color Based Image Segmentation Using K-Means Clustering," International Journal of Engineering Science and Technology, vol. 2, pp. 5319-5325, 2010.
  7. L. C.D.Ruberto, "White Blood Cells Identification and Counting from Microscopic Blood Image," World Academy of Science, Engineering and Technology, vol. 73, 2013.
  8. B. S. S. K. S. L. K. K. I.Vincent, "Feature Selection Using Principal Component Analysis for Leukemia Classification," Proceeding of the 10th International Conference on Multimedia Information Technology and Applications 2014, pp. 206-207, July 2014.