Integration of neural networks and decision tree classifiers for automated cytology screening

A squamous intraepithelial lesion (SIL) detection algorithm has been developed to process conventional Pap smears yielding a superior result (J.S.-J. Lee et al., 1990). The authors compare the object classification performance in an automated cytology screener. It consists of a Sun workstation, a DataCube image processing system, and an automatic stage/optics/illumination system. The system allows automated screening of 10 slides unattended. The main functional modules of the SIL algorithm include: image segmentation, feature extraction, and object classification. The classifiers used include neural network classifiers, statistical binary decision tree classifiers, a hybrid classifier, and the integration of multiple classifiers in an attempt to further improve algorithm performance.<<ETX>>

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