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In this study, a computer-aided diagnosis system capable of selecting a significant slice for the analysis of each nodule from a set of slices of a computed tomography (CT) scan in digital imaging and communications in medicine (DICOM) format has been developed for the diagnosis of lung cancer. First, the CT image was preprocessed by segmenting the lung parenchyma from each slice using a greedy snake algorithm. The regions of interest (ROIs) were then extracted from the lung parenchyma using a region-growing algorithm. The extracted ROIs were labelled as cancerous or non-cancerous nodules with the aid of a human expert and then the shape and texture features were extracted from each ROI. The extracted features and the label of the corresponding ROI were used to train a radial basis function neural network (RBFNN). When a CT image is given to the system for diagnosis, it is first preprocessed to extract the ROIs from each slice. Only those ROIs that are greater than nine pixels and that exist in at least three slices are considered as nodules. For each nodule, the slice with the largest area is chosen as the significant slice and this slice is taken up by the feature extraction subsystem for further analysis of the nodule. The features are extracted and fed to the RBFNN, which classifies the nodule as cancerous or non-cancerous. From the experimental results, the system was found to achieve an accuracy of 94.44%.