Robust and Efficient Diagnosis of Cervical Cancer in Pap Smear Images Using Textures Features with Rbf and Kernel Svm Classification

Classification of medical imagery is a difficult and challenging process due to the intricacy of the images and lack of models of the anatomy that totally captures the probable distortions in each structure. Cervical cancer is one of the major causes of death among other types of the cancers in women worldwide. Proper and timely diagnosis can prevent the life to some level. Consequently we have proposed an automated trustworthy system for the diagnosis of the cervical cancer using texture features and machine learning algorithm in Pap smear images , it is very beneficial to prevent cancer, also increases the reliability of the diagnosis. Proposed system is a multi-stage system for cell nucleus extraction and cancer diagnosis. First, noise removal is performed in the preprocessing step on the Pap smear images. Texture features are extracted from these noise free Pap smear images. Next phase of the proposed system is classification that is based on these extracted features, RBF and kernel based SVM classification is used. More than λ4% accuracy is achieved by the classification phase, proved that the proposed algorithm accuracy is good at detecting the cancer in the Pap smear images.