Far, BehrouzNaugler, ChristopherMohammed, Emad2013-09-162013-11-122013-09-162013http://hdl.handle.net/11023/976This thesis presents a new clinical decision support system (CDSS), which operates within an adaptive software framework and a tailored wrapper design pattern for chronic lymphocytic leukaemia (CLL) cell classification. The system goes through a sequence of steps while working with the lymphocyte images: it segments the lymphocyte with average segmentation accuracy of (97% ±0.5 for lymphocyte nucleus and 92.08% ±9.24 for lymphocyte cytoplasm); it extracts features; it selects from those features the relevant ones; and, it then classifies the selected features. The proposed system composite classifier model has a trust factor of 84.16%, accuracy of 87.0%, 84.95% true positive rate, and 10.96% false positive rate. The framework along with the wrapper pattern became a generic interface for any new algorithm. The framework built on top of the data-centric architecture which provides a great flexibility to the system design. The wrapper verifies the new algorithm interface against built-in test procedures.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.BiomedicalClinical Decision Support System (CDSS)Machine LearningAdaptive Software FrameworkClassifier FusionDempster-Shafer TheoryTailored Wrapper Design PatternChronic Lymphocytic Leukaemia (CLL)BioinformaticsWhite Blood CellsData Miningmaster thesis10.11575/PRISM/25333