Leukemia Detection Using Ensemble Model in Machine Learning

Authors

  • Arunthathi. S  Assistant Professor, Department of Biomedical Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, Tamil Nadu, India
  • Ramyaa Sri. S  UG Scholar, Department of Biomedical Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, Tamil Nadu, India
  • Deepika.S  UG Scholar, Department of Biomedical Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, Tamil Nadu, India
  • Baavana.S  UG Scholar, Department of Biomedical Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, Tamil Nadu, India

Keywords:

Machine Learning, Microscopic Analysis, Ensemble Model, Application Software.

Abstract

Leukemia is a kind of blood cancer characterised by the unregulated and abnormal synthesis of white blood cells (leukocytes) in the blood by the bone marrow. Acute lymphocytic leukaemia (ALL), acute myeloid leukaemia (AML), chronic lymphocytic leukaemia (CLL), and chronic myeloid leukaemia are the four primary kinds of leukaemia (CML). The non-specific character of Leukemia symptoms leads to incorrect diagnosis. Additionally, in microscopic investigation, leukemic cells are observed to be extremely similar to normal cells, making identification more challenging. The proposed system aims to develop leukemia detection at early stage using Machine Learning. The Thresholding technique is an existing method which is used to identify cancer cells at early stages. The method of machine learning produces considerable results in identifying the forms of leukaemia by applying the Ensemble classifier algorithm and/or regression. The Machine Learning technique is turned into development tools for diagnosing leukaemia in this suggested system, which would aid healthcare institutions in remote regions with less medical professionals, particularly in screening. It may also be changed to become open source software, which itself is available to use and share. Hence, early detection of leukaemia results in optimal therapy for the patient, ultimately lowering the mortality rate from leukaemia.

References

  1. Sos Agaian and Anthony T. Chronopoulos, "Automated Screening System for Acute Myeloid Leukaemia Detection in Blood Microscopic Images", IEEE SYSTEMS JOURNAL, vol. 8, no. 3, SEPTEMBER 2014.
  2. Emad A. Mohammed, Mostafa M. A. Mohamed, Christopher Naugler and Behrouz H. Far, "Chronic lymphocytic leukaemia cell segmentation from microscopic blood images using watershed algorithm and optimal thresholding", 26th IEEE Canadian Conference of Electrical and Computer, 2013.
  3. Fauziah Kasmin, Anton Satria Prabuwono, Azizi Abdullah, “Detection of Leukemia in Human Blood Sample Based on Microscopic Images: A Study”, Journal of Theoretical and Applied Information Technology, Vol.46, No.2, pp.579-586, 2012.
  4. Mostafa Mohamed, Behrouz Far, AmrGuaily, “An Efficient Technique for White Blood Cells Nuclei Automatic Segmentation”, IEEE International Conference on Systems, Man and Cybernetics, COEX, Seoul, Korea, October 14-17, 2012.
  5. S.N.Deepa and B.Aruna Devi, “A survey on artificial intelligence approaches for medical image classification”; IJST, vol 4, No.11 (Nov 2011).
  6. P. K. Das and S. Meher, “An efficient deep convolutional neural network based detection and classification of acute lymphoblastic leukemia,” Expert Syst. Appl., vol. 183, Nov. 2021, Art. no. 115311
  7. P. K. Das, A. Pradhan, and S. Meher, ‘‘Detection of acute lymphoblastic leukemia using machine learning techniques,’’ in Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication. Singapore: Springer, 2021, pp. 425–437.
  8. P. P. Banik, R. Saha, and K.-D. Kim, “An automatic nucleus segmentation and CNN model based classification method of white blood cell”, Expert Syst. Appl., vol. 149, Jul. 2020, Art. no. 113211.
  9. S. Mishra, B. Majhi, and P. K. Sa, ‘‘Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection,’’ Biomed. Signal Process. Control, vol. 47, pp. 303–311, Jan. 2019.
  10. R. B. Hegde, K. Prasad, H. Hebbar, B. M. K. Singh, and I. Sandhya, “Automated decision support system for detection of leukemia from peripheral blood smear images,” Journal of Digital Imaging, vol. 33, pp. 361–374, 2019
  11. L. H. Vogado, R. M. S. Veras, F. H. D. Araujo, R. R. V. Silva, and K. R. T. Aires, ‘‘Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification,’’ Eng. Appl. Artif. Intell., vol. 72, pp. 415–422, Jun. 2018.
  12. J. Zhao, M. Zhang, Z. Zhou, J. Chu, and F. Cao, “Automatic detection and classification of leukocytes using convolutional neural networks,” Medical & Biological Engineering & Computing, vol. 55, no. 8, pp. 1287–1301, 2017
  13. F. Xing and L. Yang, “Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review,” IEEE Reviews in Biomedical Engineering, vol. 9, pp. 234–263, 2016.
  14. R. G. Bagasjvara, I. Candradewi, S. Hartati, and A. Harjoko, ‘‘Automated detection and classification techniques of acute leukemia using image processing: A review,’’ in Proc. 2nd Int. Conf. Sci. Technology-Computer (ICST), Oct. 2016, pp. 35–43.
  15. Himali P. Vaghela, Hardik Modi, Manoj Pandya and M.B. Potdar, "Leukaemia Detection using Digital Image Processing Techniques", International Journal of Applied Information Systems (IJAIS), November 2015.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2023-04-30

Issue

Volume 10, Issue 2, March-April-2023

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Arunthathi. S, Ramyaa Sri. S, Deepika.S, Baavana.S "Leukemia Detection Using Ensemble Model in Machine Learning" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 2, pp.255-263, March-April-2023.