title: An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images.
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.
authors.affiliation: Computational Intelligence Research Group, Department of Computing Science and Digital Technologies, Faculty of Engineering and Environment, University of Northumbria, Newcastle, UK, NE1 8ST.
authors.forename: Siew
authors.initials: S
authors.lastname: Chin Neoh
authors.affiliation: Computational Intelligence Research Group, Department of Computing Science and Digital Technologies, Faculty of Engineering and Environment, University of Northumbria, Newcastle, UK, NE1 8ST.
authors.forename: Worawut
authors.initials: W
authors.lastname: Srisukkham
authors.affiliation: Computational Intelligence Research Group, Department of Computing Science and Digital Technologies, Faculty of Engineering and Environment, University of Northumbria, Newcastle, UK, NE1 8ST.
authors.forename: Li
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Department of Applied Sciences, Faculty of Health and Life Sciences, University of Northumbria, Newcastle, UK, NE1 8ST.
authors.forename: Stephen
authors.initials: S
authors.lastname: Todryk
authors.affiliation: Royal Victoria Infirmary Newcastle upon Tyne, UK, NE1 4LP.
authors.forename: Brigit
authors.initials: B
authors.lastname: Greystoke
authors.affiliation: Centre for Intelligent Systems Research, Deakin University, Waurn Ponds, VIC 3216, Australia.
authors.forename: Chee
authors.initials: C
authors.lastname: Peng Lim
authors.affiliation: Anglia Ruskin IT Research Institute, Faculty of Science and Technology, Anglia Ruskin University, Cambridge, CB1 1PF, UK.
authors.forename: Mohammed
authors.initials: M
authors.lastname: Alamgir Hossain
authors.affiliation: Computational Intelligence Research Group, Department of Computing Science and Digital Technologies, Faculty of Engineering and Environment, University of Northumbria, Newcastle, UK, NE1 8ST.
authors.forename: Nauman
authors.initials: N
authors.lastname: Aslam
mesh_terms: D000465:Algorithms; D002467:Cell Nucleus / Q000378:metabolism; D016000:Cluster Analysis; D003593:Cytoplasm / Q000378:metabolism; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016002:Discriminant Analysis; D006801:Humans; D007938:Leukemia / Q000097:blood / Q000175:diagnosis*; D007962:Leukocytes / Q000145:classification / Q000378:metabolism / Q000473:pathology*; D008853:Microscopy / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013194:Staining and Labeling / Q000379:methods; D060388:Support Vector Machine