The detection of tumor pixels in lung images is complex task due to its low contrast property. Hence, this paper uses deep learning architectures for both the detection and diagnosis of lung tumors in Computer Tomography (CT) images. In this article, the tumors are detected in lung CT images using Convolutional Neural Networks (CNN) architecture with the help of data augmentation methods. This proposed CNN architecture classifies the lung images into two categories as tumor images and normal images. Then, the segmentation method is used to segment the tumor pixels in the lung CT images and the segmented tumor regions are classified into either mild or severe using proposed CNN architecture.