Computer-aided diagnosis has emerged as an indispensable technique for validating the opinion of radiologists in CT interpretation. This paper presents a deep 3D convolutional neural network (CNN) architecture for automated CT scan-based lung cancer detection system. It utilizes three-dimensional spatial information to learn highly discriminative three-dimensional features instead of 2D features like texture or geometric shape which need to be generated manually. The proposed deep learning method automatically extracts the 3D features on the basis of spatiotemporal statistics. The developed model is end-to-end and is able to predict malignancy of each voxel for given input scan. Simulation results demonstrate the effectiveness of proposed 3D CNN network for classification of lung nodule inspite of limited computational capabilities.