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Intelligent leukaemia diagnosis with bare-bones PSO based feature optimization

Srisukkham, Worawut, Zhang, Li, Neoh, Siew Chin, Todryk, Stephen and Lim, Chee Peng 2017, Intelligent leukaemia diagnosis with bare-bones PSO based feature optimization, Applied soft computing, vol. 56, pp. 405-419, doi: 10.1016/j.asoc.2017.03.024.

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Title Intelligent leukaemia diagnosis with bare-bones PSO based feature optimization
Author(s) Srisukkham, Worawut
Zhang, Li
Neoh, Siew Chin
Todryk, Stephen
Lim, Chee PengORCID iD for Lim, Chee Peng
Journal name Applied soft computing
Volume number 56
Start page 405
End page 419
Total pages 15
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2017-07
ISSN 1568-4946
Keyword(s) Feature selection
Bare-bones particle swarm optimization
Acute lymphoblastic leukaemia classification
Summary In this research, we propose an intelligent decision support system for acute lymphoblastic leukaemia (ALL) diagnosis using microscopic images. Two Bare-bones Particle Swarm Optimization (BBPSO) algorithms are proposed to identify the most significant discriminative characteristics of healthy and blast cells to enable efficient ALL classification. The first BBPSO variant incorporates accelerated chaotic search mechanisms of food chasing and enemy avoidance to diversify the search and mitigate the premature convergence of the original BBPSO algorithm. The second BBPSO variant exhibits both of the abovementioned new search mechanisms in a subswarm-based search. Evaluated with the ALL-IDB2 database, both proposed algorithms achieve superior geometric mean performances of 94.94% and 96.25%, respectively, and outperform other metaheuristic search and related methods significantly for ALL classification.
Language eng
DOI 10.1016/j.asoc.2017.03.024
Field of Research 099999 Engineering not elsewhere classified
0102 Applied Mathematics
0801 Artificial Intelligence And Image Processing
0806 Information Systems
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2017, The Authors
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Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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Created: Thu, 11 May 2017, 15:57:26 EST

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