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An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images
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 segme...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4598743/ https://www.ncbi.nlm.nih.gov/pubmed/26450665 http://dx.doi.org/10.1038/srep14938 |
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author | Chin Neoh, Siew Srisukkham, Worawut Zhang, Li Todryk, Stephen Greystoke, Brigit Peng Lim, Chee Alamgir Hossain, Mohammed Aslam, Nauman |
author_facet | Chin Neoh, Siew Srisukkham, Worawut Zhang, Li Todryk, Stephen Greystoke, Brigit Peng Lim, Chee Alamgir Hossain, Mohammed Aslam, Nauman |
author_sort | Chin Neoh, Siew |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4598743 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-45987432015-10-13 An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images Chin Neoh, Siew Srisukkham, Worawut Zhang, Li Todryk, Stephen Greystoke, Brigit Peng Lim, Chee Alamgir Hossain, Mohammed Aslam, Nauman Sci Rep Article 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. Nature Publishing Group 2015-10-09 /pmc/articles/PMC4598743/ /pubmed/26450665 http://dx.doi.org/10.1038/srep14938 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Chin Neoh, Siew Srisukkham, Worawut Zhang, Li Todryk, Stephen Greystoke, Brigit Peng Lim, Chee Alamgir Hossain, Mohammed Aslam, Nauman An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images |
title | An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images |
title_full | An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images |
title_fullStr | An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images |
title_full_unstemmed | An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images |
title_short | An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images |
title_sort | intelligent decision support system for leukaemia diagnosis using microscopic blood images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4598743/ https://www.ncbi.nlm.nih.gov/pubmed/26450665 http://dx.doi.org/10.1038/srep14938 |
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