Analysis of Pap-smear Image Data

The pap-smear benchmark database provides data for comparing classification methods. The data consists of 917 images of pap-smear cells, classified carefully by cyto-technicians and doctors. The classes are difficult to separate, since class membership is not clearly defined. A basic data analysis provides numerical measures indicating how well the classes are separated, based on the Mahalanobis distance norm. The paper compares the results of three advanced classifiers against a simple minimum distance classifier. The results show that while the simple classifier provides an error rate just over 6%, error rates down to 1-2% can be achieved with a combination of feature selection together with an advanced classsifier such as ant colony optimization. Students and researchers can access the database via the Internet, and use it to test and compare their own classification methods.