Please use this identifier to cite or link to this item:
|Title:||Clinical Decision Support System with Adaptive Software Framework for Chronic Lymphocytic Leukaemia Cell Classification|
|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.|
|Appears in Collections:||Electronic Theses|
Files in This Item:
|ucalgary_2013_Mohammed_Emad.pdf||2.51 MB||Adobe PDF||View/Open|
Items in The Vault are protected by copyright, with all rights reserved, unless otherwise indicated.