ARCHIVES

Abstract: Cervical cancer leads to the fourth highest number of deaths in female cancers, carrying high risks of morbidity and mortality. Nevertheless, the cervical cancer is slow growing, so its progression through precancerous changes provides opportunities for prevention, early detection, and treatment.In this project, we propose an approach to segment the cervical cancer cells gathered from public sources using Machine Learning algorithms such as the K- means, SVM, J48, KNN are used in the project and the sample input images are selected then the segmentation is done using the Threshold based Segmentation and the nucleus and cytoplasmic segmentation is the final output of our project.In this research cell segmentation is done by morphological operations and cancer classification is done by using support vector machine (SVM). MATLAB image processing toolbox has used for coding. A dataset of 50 conventional PAP's smear slides has used for proposed method.