Improving of classification accuracy of cyst and tumor using local polynomial estimator - Nur Chamidah; Kinanti Hanugera Gusti; Eko Tjahjono; Budi Lestari (TELKOMNIKA (Telecommunication Computing Electronics and Control), 2019)
Cyst and tumor in oral cavity are seriously noticed by health experts along with increasing death cases of oral cancer in developing country. Early detection of cyst and tumor using dental panoramic image is needed since its initial growth does not cause any complaints. Image processing is done by mean for distinguishing the classification of cyst and tumor. The results in previous studies about classification of cyst and tumor were done by using a mathematical computation approach namely supports vector machine method that have still not satisfied and have not been validated. Therefore, in this study we propose a method, i.e., nonparametric regression model based on local polynomial estimator that can be improve the classification accuracy of cyst and tumor on human dental panoramic image. By using the proposed method, we get the classification accuracy of cyst and tumor, i.e., 90.91% which is greater than those by using the support vector machine method, i.e., 76.67%. Also, in validation process we obtain that the nonparametric regression model approach gives a significant Press’s Q statistical testing value. So, we conclude that the nonparametric regression model approach improves the classification accuracy and gives better outcome to classify cyst and tumor using dental panoramic image than the support vector machine method.