Clinical Decision Support System with Adaptive Software Framework for Chronic Lymphocytic Leukaemia Cell Classification
Date
2013-09-16
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Abstract
This 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.
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Biomedical
Citation
Mohammed, E. (2013). Clinical Decision Support System with Adaptive Software Framework for Chronic Lymphocytic Leukaemia Cell Classification (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25333