title: Estimating false positive and false negative error rates in cervical cell classification.
abstract: The performance of a cell recognition system on unknown data is often estimated in terms of its error rates on a test set. This paper investigates methods for producing estimates of error rates in cervical cell classification. Classification performance curves calculated using these methods are given for several classification schemes used to classify 1500 cervical cells.
mesh_terms: D001322:Autoanalysis; D002467:Cell Nucleus; D002584:Cervix Uteri / Q000166:cytology* / Q000473:pathology; D003584:Cytological Techniques; D003593:Cytoplasm; D005188:False Negative Reactions; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D002583:Uterine Cervical Neoplasms / Q000473:pathology*