Lung cancer is very effective and causes deadly diseases among human. It is detected in the last stage, not in the initial stage, which can give the worst results. It also defects other parts of the body. Lung cancer has two types i.e non-small lung cancer and small lung cancer. Smoking can be one of the main reason of lung cancer. This can be diagnosis by the different techniques Many techniques were used to detect lung cancer, but CT is better for accurate results using imaging techniques. CT scan is helpful in predicting the performance of classification. Classification plays a major role in image analysis. Firstly, ct scan image as an input pass to the system through the image processing and then segmentation is performed. The main components of the structure is ct scan images, segmentation, nodule detection, feature extraction. Training data and testing data is used to determine the accuracy of both the algorithms and check the better accuracy. The main objective is to evaluate the computed tomography technique through SVM and then compare it with Naive Bayes to increase the accuracy, but there are some limitations to reach 100%.