TY - JFULL AU - Guo Xiuhua and Sun Tao and Wu Haifeng and He Wen and Liang Zhigang and Zhang Mengxia and Guo Aimin and Wang Wei PY - 2010/12/ TI - Support Vector Machine Prediction Model of Early-stage Lung Cancer Based on Curvelet Transform to Extract Texture Features of CT Image T2 - International Journal of Biomedical and Biological Engineering SP - 538 EP - 543 VL - 4 SN - 1307-6892 UR - https://publications.waset.org/pdf/14387 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 47, 2010 N2 - Purpose: To explore the use of Curvelet transform to extract texture features of pulmonary nodules in CT image and support vector machine to establish prediction model of small solitary pulmonary nodules in order to promote the ratio of detection and diagnosis of early-stage lung cancer. Methods: 2461 benign or malignant small solitary pulmonary nodules in CT image from 129 patients were collected. Fourteen Curvelet transform textural features were as parameters to establish support vector machine prediction model. Results: Compared with other methods, using 252 texture features as parameters to establish prediction model is more proper. And the classification consistency, sensitivity and specificity for the model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based on texture features extracted from Curvelet transform, support vector machine prediction model is sensitive to lung cancer, which can promote the rate of diagnosis for early-stage lung cancer to some extent. ER -