Classification of atypical cells in the automatic cytoscreening for cervical cancer

One of the pattern recognition problems that is of importance in automated cytology is the detection of abnormal cells that may be present in a sample taken from a person. This work describes the problem and some possible solutions within the context of the detection of pre-cancer abnormalities in samples that have been prepared as Pap smears for observation under light microscopy. A cell classification system must be capable of using information concerning the existence of various subclasses of normal and abnormal cells. The classification of multi-modal data can be modeled using the Bayesian decision model with knowledge of the various subclasses composing each of the classes being decided. Two decision rules are shown which are applicable to this problem, and are suitable for the classification required for automated detection of atypical cells in a cervical smear. Test results from a series of holdout experiments indicate that average correct recognition rates of about 85% can be achieved on the atypical cells, while maintaining an error rate of about 1% on the normal cells.