Computer aided diagnosis (CAD) is started to be implemented broadly in the diagnosis and detection of many varieties of abnormalities acquired during various imaging procedures. The main aim of the CAD systems is to increase the accuracy and decrease the time of diagnoses, while the general achievement for CAD systems are to find the place of nodules and to determine the characteristic features of them. As lung cancer is one of the fatal and leading cancer types, there has been plenty of studies for the usage of the CAD systems to detect lung cancer. Yet, the CAD systems need to be developed a lot to identify the different shapes of nodules, lung segmentation and to have higher level of sensitivity, specifity and accuracy. In this paper, Lung Image Database Consortium (LIDC) database is used which comprises of an image set of lung cancer thoracic documented CT scans. After performing image pre-processing, segmentation, feature extraction/selection steps, classification is utilized using Support Vector Machine (SVM) with Gaussian RBF and 97.3% of specificity and 92.0% of sensitivity is achieved which is superior to recently proposed CAD systems.