Pulmonary cancer is one of the major causes of deaths caused by cancer around the globe. Early stage lung cancer detection can prove to be essential for the patients, for which the computed tomography (CT) images are analyzed by the radiologists to determine the presence of nodules and diagnose the disease. Conventional techniques used by the radiologists for nodule detection in CT images is time-consuming and inefficient; to assist in the diagnosis process and further enhance its efficiency and accuracy, decision support systems have been developed in the past few years. In our paper, we proposed a Multi-Dimension Region-based Fully Convolutional Network based decision support system for detection and classification of lung nodule. The Multi-Dimension RFCN serves as an image classifier backbone for our feature extraction step in addition to the proposed Tri-Level Region Proposal Network (3L-RPN) along with the position-sensitive score maps (PSSM) being explored. A novel median intensity projection method is used to leverage the multi-dimensional information from CT images and introduced an additional deconvolutional layer to adopt the proposed Tri-Level Region Proposal Network in our architecture to automatically identify the potential Region of Interest. We trained and evaluated our proposed decision support system using LIDC-IDRI dataset. The evaluation results demonstrated the high level performance of our proposed model in comparison to the state-of-the-art nodule detection and classification methods by attaining classification accuracy of 97.61% and sensitivity of 97.4%.