Article: Acute myelogenous leukaemia detection in blood microscope images using particle swarm optimisation Journal: International Journal of Computational Vision and Robotics (IJCVR) 2024 Vol.14 No.3 pp.250 - 263 Abstract: The acute myelogenous leukaemia (AML) is one of the types of acute leukaemia that is seen in adults. Nowadays, people use manual tests of blood smear to diagnose leukaemia. This manual method requires more time and the operator's ability to diagnose the diseases. In this article, a new hybrid technique that detects AML in blood smears is presented. The proposed method uses a texture-based method - local binary pattern (LBP) and a statistical-based method - grey-level co-occurrence matrix (GLCM) to extract the features from WBC cells. The best features are selected by using a PSO algorithm and their accuracy is measured using nearest neighbour (NN)-classifier and extreme learning machine (ELM). The proposed method was tested using American Society of Hematology (ASH) public datasets and achieved promising results. The ASH database consists of 80 images, where 40 images are taken from AML patients and the remaining 40 are from non-AML patients. The proposed method, LBP+GLCM+PSO along with the ELM classifier achieved an accuracy of 90.44%. The experiment shows that the proposed method outperforms the existing methods in the detection of AML. Inderscience Publishers - linking academia, business and industry through research

Title: Acute myelogenous leukaemia detection in blood microscope images using particle swarm optimisation

Authors: Abdullah Mohan; Kedir Beshir; Alemayehu Kebede

Addresses: Department of Electrical and Computer Engineering, Wachemo University, Hossana, Ethiopia ' Department of Electrical and Computer Engineering, Wachemo University, Hossana, Ethiopia ' Department of Electrical and Computer Engineering, Wachemo University, Hossana, Ethiopia

Abstract: The acute myelogenous leukaemia (AML) is one of the types of acute leukaemia that is seen in adults. Nowadays, people use manual tests of blood smear to diagnose leukaemia. This manual method requires more time and the operator's ability to diagnose the diseases. In this article, a new hybrid technique that detects AML in blood smears is presented. The proposed method uses a texture-based method - local binary pattern (LBP) and a statistical-based method - grey-level co-occurrence matrix (GLCM) to extract the features from WBC cells. The best features are selected by using a PSO algorithm and their accuracy is measured using nearest neighbour (NN)-classifier and extreme learning machine (ELM). The proposed method was tested using American Society of Hematology (ASH) public datasets and achieved promising results. The ASH database consists of 80 images, where 40 images are taken from AML patients and the remaining 40 are from non-AML patients. The proposed method, LBP+GLCM+PSO along with the ELM classifier achieved an accuracy of 90.44%. The experiment shows that the proposed method outperforms the existing methods in the detection of AML.

Keywords: NN-classifier; particle swarm optimisation; PSO; acute myelogenous leukaemia; AML; acute lymphoblastic leukaemia; ALL.

DOI: 10.1504/IJCVR.2024.138308

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.3, pp.250 - 263

Received: 25 Dec 2021
Accepted: 22 Aug 2022
Published online: 01 May 2024 *

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Title: Acute myelogenous leukaemia detection in blood microscope images using particle swarm optimisation

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