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An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images

Lookup NU author(s): Dr Stephen Todryk, Dr Brigit Greystoke

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

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.


Publication metadata

Author(s): Neoh SC, Srisukkham W, Zhang L, Todryk S, Greystoke B, Lim CP, Hossain MA, Aslam N

Publication type: Article

Publication status: Published

Journal: Scientific Reports

Year: 2015

Volume: 5

Online publication date: 09/10/2015

Acceptance date: 28/08/2015

Date deposited: 07/06/2016

ISSN (print): 2045-2322

Publisher: Nature Publishing Group

URL: http://dx.doi.org/10.1038/srep14938

DOI: 10.1038/srep14938


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