Cervical cancer detection and classification using Independent Level sets and multi SVMs

Introduced in 1940, Pap smear test has proven to be an effective screening method to determine the different stages of cervical cancer. Identification and classification of Pap smear images to detect cervical cancer via manual screening is a challenging task for pathologists therefore increasing the chances of human error. In this paper, we propose an automatic method to detect and classify the grade of cervical cancer using both geometric and texture features of Pap smear images and classifying accordingly using multi SVM. The geometric features are obtained through segmentation of nucleus and cytoplasm using independent level sets, detecting whether the cell is cancerous or normal, with reference to the ground truth. By extracting well defined GLCM texture features and using a combination of PCA and the best class of multi SVM, the images are classified with an accuracy of 95%.