OPTIMIZING CERVICAL CELL CLASSIFIERS

  • 1 January 1980
    • journal article
    • research article
    • Vol. 2  (2) , 117-122
Abstract
In an automated prescreening system [i.e., human cervical cancer screening] where a cell classifier and a specimen classifier operate in cascade, the false-positive and false-negative error rates of each classifier can be traded off to obtain the best overall performance. It is usually desirable to keep the specimen false-negative rate below the false-positive rate. An analysis of the classifier cascade shows that, in contrast, the cell classifier should have its false positive rate much lower than its false-negative rate. A procedure is presented for selecting the best operating point on the [receiver operating characteristic] curve of the cell classifier. This minimizes the sample size required to achieve prescribed specimen error rates.

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