IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 9 - Issue 1, January 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Machine Learning Algorithms For Diagnosis Of Leukemia

[Full Text]

 

AUTHOR(S)

Italia Joseph Maria, T. Devi, D. Ravi

 

KEYWORDS

Comparison of Machine Learning Algorithms, Leukemia Diagnosis, Leukemia Classification, Machine Learning

 

ABSTRACT

Leukemia is cancer of the blood, which includes the bone marrow and the lymphatic tissues, usually involving white blood cells. Unlike usual cancer, leukemia does not form solid tumours, but form large number of abnormal white blood cells which crowd out the normal blood cells. Machine Learning algorithms are largely employed in the treatment of Leukemia, be it for classification of different leukemia types or for detecting if leukemia is present in a patient. This paper describes Support Vector Machines, k-Nearest Neighbour, Neural Networks, Naïve Bayes and Deep Learning algorithms which are used to classify leukemia into its sub-types and presents a comparative study of these algorithms.

 

REFERENCES

[1] J. Laosai and K. Chamnongthai. "Acute leukemia classification by using SVM and K-Means clustering" Proceedings of the International Electrical Engineering Congress pp. 1-4. 2014
[2] “Types of leukemia”15 Nov. 2019
[3] Subhan, Ms. Parminder Kaur. “Significant Analysis of Leukemic Cells Extraction and Detection Using KNN and Hough Transform Algorithm” International Journal of Computer Science Trends and Technology, vol. 3, no.1, pp. 27-33, 2015
[4] Supardi, N. Z., Mashor, M. Y., Harun, N. H., Bakri, F. A., & Hassan, R. “Classification of blasts in acute leukemia blood samples using k-nearest neighbour”. IEEE 8th International Colloquium on Signal Processing and Its Applications, pp. 461-65, 2012
[5] Vincent, I., Kwon, K.-R., Lee, S.-H., & Moon, K.-S. (2015). “Acute lymphoid leukemia classification using two-step neural network classifier” 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Jan. 2015
[6] Adjouadi, M., Ayala, M., Cabrerizo, M. et al. “Classification of Leukemia Blood Samples Using Neural Networks” Annals of Biomedical Engineering, vol. 38, no. 4, pp. 1473-82, Apr.2010
[7] Gautam, A., Singh, P., Raman, B., & Bhadauria, H. “Automatic classification of leukocytes using morphological features and Naïve Bayes classifier” IEEE Region 10 Conference (TENCON), pp.1023-27, Nov. 2016
[8] Rehman, A., Abbas, N., Saba, T., Rahman, S. I. ur, Mehmood, Z., & Kolivand, H. “Classification of acute lymphoblastic leukemia using deep learning” Microscopy Research and Technique, vol. 81, no. 11, 23 Oct. 2018
[9] “Occam's razor” 17 Nov. 2019