lim-intelligentleukaemia-2017.pdf (1.66 MB)
Intelligent leukaemia diagnosis with bare-bones PSO based feature optimization
journal contribution
posted on 2017-07-01, 00:00 authored by W Srisukkham, L Zhang, S C Neoh, S Todryk, Chee Peng LimChee Peng LimIn 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 computingVolume
56Pagination
405 - 419Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
Link to full text
ISSN
1568-4946Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2017, The AuthorsUsage metrics
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Keywords
Feature selectionBare-bones particle swarm optimizationAcute lymphoblastic leukaemia classificationScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Interdisciplinary ApplicationsComputer ScienceAcute lymphoblastic leukaemiaclassificationPARTICLE SWARM OPTIMIZATIONACUTE LYMPHOBLASTIC-LEUKEMIAFEATURE-SELECTIONCUCKOO SEARCHALGORITHMSYSTEMDISEASEIMAGESMODELInformation SystemsArtificial Intelligence and Image Processing
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