Segmentation is an essential step in image systems for the accurate lung disease diagnosis, since it delimits lung structures in Computerised Tomography (CT) images. Indeed, image processing techniques can help computer diagnosis if lung region is accurately obtained. A conventional fuzzy cmeans clustering algorithm that has been implemented for segmentation of the Computerised Tomography (CT) lung images still suffers with low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. The proposed system presents an intelligent and dynamic approach called Intelligent Fuzzy C-Means (IFCM) to segment the lung nodules automatically and classify the lung nodules effectively using support vector machine classifier. This approach uses the capability of firefly search to find optimal initial cluster centers for the Fuzzy C-Means (FCM) and thus improve the segmentation accuracy. The features are extracted using fused tamura and haralick features after segmentation. These features are trained using different kernels of support vector machine for automatic detection of lung nodules as benign or malignant. The performance of support vector machine is evaluated by computing different measures from confusion matrix.