Identifying the exact pulmonary nodule boundaries in computed tomography (CT) images are crucial tasks to computer-aided detection systems (CADx). Segregation of CT images as benign, malignant and non-cancerous is essential for early detection of lung cancers to improve survival rates. In this paper, a methodology for automated tumor stage classification of pulmonary lung nodules is proposed using an end-to-end learning Deep Convolutional Neural Network (DCNN). The images used in the study were acquired from the Lung Image Database Consortium and Infectious Disease Research Institute (LIDC-IDRI) public repository comprising of 1018 cases. Lung CT images with candidate nodules are segmented into a 52 × 52 pixel nodule region of interest (NROI) rectangle based on four radiologists’ annotations and markings with ground truth (GT) values. The approach aims in analyzing and extracting the self-learned salient features from the NROI consisting of differently structured nodules. DCNN are trained with NROI samples and are further classified according to the tumor patterns as non-cancerous, benign or malignant samples. Data augmentation and dropouts are used to avoid overfitting. The algorithm was compared with the state of art methods and traditional hand-crafted features like the statistical, texture and morphological behavior of lung CT images. A consistent improvement in the performance of the DCNN was observed using nodule grouped dataset and the classification accuracy of 97.8%, the specificity of 97.2%, the sensitivity of 97.1%, and area under the receiver operating characteristic curve (AUC) score of 0.9956 was achieved with reduced low false positives.