Comparison of Segmentation Algorithms for Leukemia Classification

Sunita Chand; Virendra P Vishwakarma

Proceedings of the First International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India

Research Article

Comparison of Segmentation Algorithms for Leukemia Classification

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@INPROCEEDINGS{10.4108/eai.16-5-2020.2303967,
    author={Sunita  Chand and Virendra P  Vishwakarma},
    title={Comparison of Segmentation Algorithms for Leukemia Classification},
    proceedings={Proceedings of the First  International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India},
    publisher={EAI},
    proceedings_a={ICASISET},
    year={2021},
    month={1},
    keywords={acute leukemia machine learning support vector machine image processing image segmentation},
    doi={10.4108/eai.16-5-2020.2303967}
}

Sunita Chand
Virendra P Vishwakarma
Year: 2021
Comparison of Segmentation Algorithms for Leukemia Classification
ICASISET
EAI
DOI: 10.4108/eai.16-5-2020.2303967

Sunita Chand¹^,*, Virendra P Vishwakarma¹

1: University School of Information and Communication Technology, Guru Gobind Singh University, New Delhi, India

*Contact email: sunitamk@gmail.com

Abstract

Leukemia is a deadly cancer that results from the proliferation of non-differentiated white blood cells in blood as compared to the other two types of cells, i.e., red blood cells and platelets. These cells are known as blasts cells which overcrowd other cells rendering those cells as inefficient in their functions and are are themselves non-functional. This paper presents a comparative study of four different segmentation techniques on the images of peripheral blood smear and the classification of these images into diseased and healthy cells using the SVM classifier. The best result was obtained by a custom threshold method of segmentation with a classification accuracy of 96.89%.

Keywords: acute leukemia machine learning support vector machine image processing image segmentation

Published: 2021-01-27
Publisher: EAI

: http://dx.doi.org/10.4108/eai.16-5-2020.2303967