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

Neoh, Siew Chin, Srisukkham, Worawut, Zhang, Li, Todryk, Stephen, Greystoke, Brigit, Peng Lim, Chee, Hossain, Alamgir and Aslam, Nauman (2015) An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images. Scientific Reports, 5 (14938). ISSN 2045-2322

Text (Full text)
design and leukaemia diagnosis.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (1MB) | Preview
Official URL:


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 conjunction 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 subimages 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.

Item Type: Article
Uncontrolled Keywords: Data mining, Image processing, Machine learning
Subjects: B800 Medical Technology
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 20 Oct 2015 11:02
Last Modified: 01 Aug 2021 01:01

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics