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

Siew Chin Neoh, Worawut Srisukkham, Li Zhang, Stephen Todryk, Brigit Greystoke, Chee Peng Lim, Mohammed Alamgir Hossain, Nauman Aslam

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22 Citations (Scopus)
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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.

Original languageEnglish
Article number14938
JournalScientific Reports
Volume5
DOIs
Publication statusPublished - 9 Oct 2015

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Cluster Analysis
Cytoplasm
Leukemia
Lymphocytes
Neural Networks (Computer)
Discriminant Analysis
Precursor Cell Lymphoblastic Leukemia-Lymphoma
Color
Databases
Research
Support Vector Machine

Cite this

Chin Neoh, S., Srisukkham, W., Zhang, L., Todryk, S., Greystoke, B., Lim, C. P., ... Aslam, N. (2015). An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images. Scientific Reports, 5, [14938]. https://doi.org/10.1038/srep14938
Chin Neoh, Siew ; Srisukkham, Worawut ; Zhang, Li ; Todryk, Stephen ; Greystoke, Brigit ; Lim, Chee Peng ; Hossain, Mohammed Alamgir ; Aslam, Nauman. / An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images. In: Scientific Reports. 2015 ; Vol. 5.
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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.",
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Chin Neoh, S, Srisukkham, W, Zhang, L, Todryk, S, Greystoke, B, Lim, CP, Hossain, MA & Aslam, N 2015, 'An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images', Scientific Reports, vol. 5, 14938. https://doi.org/10.1038/srep14938

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; Aslam, Nauman.

In: Scientific Reports, Vol. 5, 14938, 09.10.2015.

Research output: Contribution to journalArticleResearchpeer-review

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T1 - An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images

AU - Chin Neoh, Siew

AU - Srisukkham, Worawut

AU - Zhang, Li

AU - Todryk, Stephen

AU - Greystoke, Brigit

AU - Lim, Chee Peng

AU - Hossain, Mohammed Alamgir

AU - Aslam, Nauman

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N2 - 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.

AB - 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.

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Chin Neoh S, Srisukkham W, Zhang L, Todryk S, Greystoke B, Lim CP et al. An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images. Scientific Reports. 2015 Oct 9;5. 14938. https://doi.org/10.1038/srep14938