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An intelligent decision support system for leukaemia diagnosis using microscopic blood images

Chin Neoh, Siew, Srisukkham, Worawut, Zhang, Li, Todryk, Stephen, Greystoke, Brigit, Lim, Chee Peng, Hossain, Mohammed Alamgir and Aslam, Nauman 2015, An intelligent decision support system for leukaemia diagnosis using microscopic blood images, Scientific reports, vol. 5, pp. 1-14, doi: 10.1038/srep14938.

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Title An intelligent decision support system for leukaemia diagnosis using microscopic blood images
Author(s) Chin Neoh, Siew
Srisukkham, Worawut
Zhang, Li
Todryk, Stephen
Greystoke, Brigit
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Hossain, Mohammed Alamgir
Aslam, Nauman
Journal name Scientific reports
Volume number 5
Start page 1
End page 14
Total pages 14
Publisher Nature Publishing Group
Place of publication London, Eng.
Publication date 2015-10-09
ISSN 2045-2322
Summary This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.
Language eng
DOI 10.1038/srep14938
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30079902

Document type: Journal Article
Collections: Centre for Intelligent Systems Research
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.