Classification Of Cervical Cancer Stage From Pap Smear Tests

This research focuses on the field of biomedical engineering in the works of Pap smear image analysis. Pap smear test is an efficient procedure in detecting cases of cervical cancer especially in early stages. However, most of these tests are done manually by medical personnel, which remains a tedious task to carry out on a daily basis due to the occurrence of human and technical error. The purpose of this research is to identify an effective algorithm to classify the presence of abnormalities in the given Pap smear samples. The proposed approach will implement stages of image pre-processing, feature selection and extraction as well as classification of classes. During image preprocessing, the image will be converted to greyscale before improving their contrast level for better analysis. Feature extraction is then used to select the appropriate features that contribute most to the predicted variable from the image. Then, classification methods for the classification of classes in these cells such as K-Nearest Neighborhood (KNN) and Support Vector Machine (SVM) were explored. The performance of the proposed classification algorithm gave satisfactory results of accuracy, 91.9% for KNN classification and 95.0% for SVM classification