Applications of Machine Learning and Image Processing Techniques in the Detection of Leukemia

Siddhika Arunachalam¹

1. Dept. of Computer Engineering, Sardar Patel Institute of Technology, University of Mumbai, Mumbai, India.

Correspondence should be addressed to: siddhi97am@gmail.com, Tel.: +919969286067.

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.2 , pp.77-82, Apr-2020

Online published on Apr 30, 2020

Copyright © Siddhika Arunachalam . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The cancer of blood and bone marrow is called Leukemia. It is a result of the uncontrolled reproduction of immature white blood cells. It hampers the ability of the body to fight infection. In Leukemia, the white blood cells (WBC) are generally affected. There are different types of Leukemia namely: Acute myelogenous Leukemia (AML), Acute lymphocytic Leukemia (ALL), Chronic myelogenous Leukemia (CML), Chronic lymphocytic Leukemia (CLL). This paper proposes to automate the Leukemia detection process using machine learning and different techniques of image processing. The dataset consists of images of blood smear which is of both Leukemia and non-Leukemia patients. K-means Clustering, Marker-Controlled Watershed segmentation, and HSV colour-based segmentation are the image segmentation algorithms that have been used. Various features from the segmented lymphocyte images are extracted since the structural components of normal and Leukemic lymphocytes differ remarkably. The SVM classifier, which is a machine learning technique, is used to further classify Leukemia into its different types. This paper aims at identifying Leukemia and determine its types whether it is AML, ALL, CML or CLL which takes the classification process one step further as a majority of the previous works have been restricted to just detection of Leukemia or classifying into few of the main subtypes. The proposed system is successfully implemented using MATLAB.

Key-Words / Index Term :
Leukemia; Image Processing; Machine Learning; SVM Classifier; MATLAB

References :
[1] Tathagata Hazra, Mrinal Kumar, Sanjaya Shankar Tripathy, “Automatic Leukemia Detection Using Image Processing Technique”, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), Vol. VI, Issue IV, April 2017, pp 42.
[2] Sachin Paswan, Yogesh Kumar Rathore, “Detection and Classification of Blood Cancer from Microscopic Cell Images Using SVM KNN and NN Classifier”, International Journal of Advanced Research, Ideas and Innovations in Technology, Vol. 3, Issue 6.
[3] Jameela Ali,AbdulRahim Ahmad, Loay E. George,Chen Soong Der,Sherna Aziz, “Red Blood Cell Recognition using Geometrical Features”, IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 1, January 2013.
[4] Sarmad Shafique, Samabia Tehsin, “Computer-Aided Diagnosis of Acute Lymphoblastic Leukemia”, Hindawi, Computational and Mathematical Methods in Medicine Volume 2018, 13 pages.
[5] Chaitali Raje, Jyoti Rangole, “Detection of Leukemia in Microscopic Images Using Image Processing”, International Conference on Communication and Signal Processing, April 3-5, 2014, India, IEEE.
[6] Nimesh Patel, Ashutosh Mishra, “Automated Leukemia Detection Using Microscopic Images”, Procedia Computer Science 58 (2015) 635 – 642, Published by Elsevier B.V.
[7] R.G Bagasjvara, Ika Candradewi, Sri Hartati, Agus Harjoko, “Automated Detection and Classification Techniques of Acute Leukemia using Image Processing: A Review”, 2016 2nd International Conference on Science and Technology-Computer (ICST), Yogyakarta, Indonesia, 978-1-5090-4357-6/2016 IEEE.
[8] S.Srimathi, G.Yamuna, “Study of Cancer Detection Techniques Using Various Image Processing Algorithms”, International Journal of Engineering Development and Research (www.ijedr.org), IJEDR 2018, Vol. 6, Issue 3.
[9] Preeti Jagdev, “Automated Detection and Classification of Leukemia Using Image Processing and Machine Learning”, International Journal of Advance Research in Science and Engineering, Vol. 6, Issue 4, April, 2017.
[10] Berge, Heidi, Dale Taylor, Sriram Krishnan, and Tania S.Douglas. "Improved red blood cell counting in thin blood smears." In Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, pp. 204-207.
[11] H. Ramoser, V. Laurain, H. Bischof, Rupert Ecker, “Leukocyte segmentation and classification in blood-smear images,” Proc. IEEE-EMBS, 2005, pp. 3371-3374.
[12] M. Subrajeet, D. Patra, and S. Satpathy, “Automated Leukemia detection in blood microscopic images using statistical texture analysis,” Proc. of the 2011 International Conference on Communication, Computing & Security, pp. 184-187. ACM, 2011.