In the support vector machine (SVM) method, the membership function has a vital infection on the classification of samples. Due to the limitation of the function own condition, this method cannot effectively distinguish the noise and outliers samples. A fuzzy support vector machine (FSVM) was developed based on the dual membership method to solve the problem. The method uses the characteristics of a specifically medical image to map the membership function which has been obtained from the method of degree membership to two different sides, mapping the membership function to obtain the membership function which can more effectively analyze the specific sample. The improved fuzzy support vector method was used to classify benign and malignant of the pulmonary nodule. The parameters inversion shows that the developed method distinguishes the noise and outlier samples more effectively, compared with the traditional fuzzy support vector machine method, and solves the over-fitting problem of traditional methods. Therefore, the results illustrate the robustness to anti-noise property and the effective classification ability of the developed method.