The Computer Aided Diagnosing (CAD) system is proposed in this paper for detection of lung cancer form the analysis of computed tomography (CT) images of chest. To produce a successful Computer Aided Diagnosissystem, several problems has to be resolved. Segmentation is the first problem to be considered which helps in generation of candidate region for detecting cancer nodules. The second problem is identification of affected nodules from all the candidate nodules. Initially, the basic image processing techniques such as Bit-Plane Slicing, Erosion, Median Filter, Dilation, Outlining, Lung Border Extraction and Flood-Fill algorithms are applied to the CT scan image in order to detect the lung region. Then the segmentation algorithm is applied in order to detect the cancer nodules from the extracted lung image. In this paper, Fuzzy Possibilistic C Mean (FPCM) algorithm is used for segmentation because of its accuracy. After segmentation, rule based technique is applied to classify the cancernodules. Finally, a set of diagnosis rules are generated from the extracted features. From these rules, the occurrences of cancer nodules are identified clearly. The learning is performed with the help of Extreme Learning Machine (ELM) because of its better classification. For experimentation of the proposed technique, the CT images are collected from reputed hospital. The proposed system can be able to detect the false positive nodules accurately.