Dataset Information


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.


PROVIDER: S-EPMC4598743 | BioStudies | 2015-01-01

SECONDARY ACCESSION(S): 10.1038/srep14938

REPOSITORIES: biostudies

Similar Datasets

2017-01-01 | S-EPMC5539540 | BioStudies
2013-01-01 | S-EPMC3602891 | BioStudies
2006-01-01 | S-EPMC1891684 | BioStudies
2017-01-01 | S-EPMC5722377 | BioStudies
1000-01-01 | S-EPMC6263950 | BioStudies
2019-01-01 | S-EPMC6342330 | BioStudies
2019-01-01 | S-EPMC6723724 | BioStudies
2020-01-01 | S-EPMC7206335 | BioStudies
2013-01-01 | S-EPMC3569426 | BioStudies
2014-01-01 | S-EPMC4165327 | BioStudies