Comparison of Segmentation Algorithms for Leukemia Classification

Chand, Sunita and Vishwakarma, Virendra P (2021) Comparison of Segmentation Algorithms for Leukemia Classification. In: ICASISET 2020, 16-17 May 2020, Chennai, India.

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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%.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: acute leukemia machine learning support vector machine image processing image segmentation
Subjects: Q Science > QA Mathematics > QA76 Computer software
Depositing User: EAI Editor III.
Date Deposited: 09 Mar 2021 09:48
Last Modified: 09 Mar 2021 09:48
URI: https://eprints.eudl.eu/id/eprint/1395

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