In recent times, current healthcare imaging investigation, both for analytical and treatment reasons involves Artificial Intelligence (AI). AI has been used by research scientists to intelligently identify intricate structures in imaging data and provide statistical evaluations of diagnostic properties. AI has been employed in the field of cancer research on various image modalities that are used throughout different phases of the therapy such as tumor detection and therapeutic evaluation. AI is the key enabler for analyzing one such image modality, Computed Tomography (CT) revealing illness characteristics that are invisible to the human vision. One of the other increasingly popular subjects for research in medical imaging currently is radiomics, which is the retrieval of a large number of image characteristics from radiation imagery using a highly efficient methodology. The present research is aimed at integrating AI and radiomics for the medical image analysis of CT images for detecting pulmonary tumors. A novel method, AIDCNN, which is an AI based Deep Convolutional Neural Network is proposed to investigate the CT images. Dice index is used as a metric to evaluate the performance of the suggested approach. The outcomes of AIDCNN model is compared conventional deep learning based CNN models to demonstrate the performance superiority of the proposed method.