Abstract: 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 completely captures the possible deformations in each structure. Cervical cancer is one of the major causes of death among other types of the cancers in women world wide. Proper and timely diagnosis can prevent the life to some extent. Therefore we have proposed an automated reliable 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. In this research study, we have developed, multi class cervical cancer severity analysis system based on hybrid texture features and hybrid RBF kernel based support vector machine using PAP smear images. Two major contribution of the proposed system is feature extraction and feature classification. In feature extraction, multiple features are extracted using texture features and Gabor filter based orientation image. This system classifies the PAP smear cells into anyone of four different types of classes using RBF-SVM. The performance of the proposed algorithm is tested and compared to other algorithms on public image database of Herlev University Hospital, Denmark with 452 PAP smear images. The overall classification accuracy of the proposed hybrid RBF-SVM is 96.8% but the existing methods RBF and SVM produce 91.32 and 94.32%, respectively.
G. Hariharan, A. Jayachandran, G. Jiji, M. Rajaram and T. Ajith Bosco Raj, 2016. Severity Analysis of Cervical Cancer in PAP Smear Images Using Textures Features and Hybrid RBF Kernel Based SVM. Asian Journal of Information Technology, 15: 2167-2176.