Comparison of Feature selection methods for diagnosis of cervical cancer using SVM classifier
Even though a great attention has been given on the cervical cancer diagnosis, it is a tuff task to observe the pap smear slide through microscope. Image Processing and Machine learning techniques helps the pathologist to take proper decision. In this paper, we presented the diagnosis method using cervical cell image which is obtained by Pap smear test. Image segmentation performed by multi thresholding method and texture and shape features are extracted related to cervical cancer. Feature selection is achieved using Mutual Information(MI) Sequential Forward Search (SFS) Sequential Floating Forward Search (SFFS) and Random Subset Feature Selection(RSFS) methods.