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Intelligent leukaemia diagnosis with bare-bones PSO based feature optimization

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Version 2 2024-06-06, 08:08
Version 1 2017-05-11, 15:57
journal contribution
posted on 2024-06-06, 08:08 authored by W Srisukkham, L Zhang, SC Neoh, S Todryk, Chee Peng LimChee Peng Lim
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.

History

Journal

Applied soft computing

Volume

56

Pagination

405-419

Location

Amsterdam, The Netherlands

Open access

  • Yes

ISSN

1568-4946

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2017, The Authors

Publisher

Elsevier