Cervical cancer is the highest rate of incidence after breast cancer, gastric cancer, colorectal cancer, thyroid cancer among all malignant cancers that occurs to females; also it is the most prevalent cancer among female genital cancers. Manual cervical cancer diagnosis methods are costly and sometimes result in inaccurate diagnosis caused by human error but machine assisted classification system can reduce financial costs and increase screening accuracy. In this research article, we have developed, multi class cervical cancer classification system based on hybrid texture features and fuzzy logic based support vector machine using Pap smear images. Two major contribution of the proposed system is feature extraction & its classification. In feature extraction, multiple features are extracted using multi texton histogram and Gabor filter based orientation image. This system classifies the Pap smear cells into anyone of four different types of classes using Fuzzy-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 MMTH+ Fuzzy HKSVM is 96.8%, but the existing methods MMTH+RBF and MMTH+SVM produce 91.32 % and 94.32% respectively.
