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

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