Optimizing cervical specimen classifiers

In an automated cervical cancer screening program, a prescreening machine could pass suspicious specimens to a cytotechnologist or cytopathologist while eliminating the normals from human consideration. This decision should be made at minimum cost consistent with acceptable false negative error rates. The sequential probability ratio test allows the designer to specify the probability of detection, select the false positive rate to minimize the overall cost of the test, and determine the expected cost of operating the system. The paper formulates that approach and presents specific examples based on actual cell classification experiments to illustrate the trade-off between operating cost and probability of detection.