## 2 Review of the Literature  
The detection and segmentation of lung images are aggressive research areas where many promising results are received. The research results are very much helpful in determining the medicines and to study their effectiveness [1; 4]. Textural analyses of medical images are also very helpful in identifying the tissues abnormalities. Accuracy of the resultsholds a very important role in the recovery of the patient. The best observations result in prescribing the best medicines.  
Segmentation can be done manually, but the chances of obtaining inaccurate results are much higher. Semi-automated segmentation is also available. It is error free when compared with the former method. For fast and accurate results, it is desirable to use an automatic segmentation process [5; 6; 7; 8; 9; 10; 11]. There are so many deep learning experiments and research taking place. A drastic development has occurred in the area of classification segmentation and detection of nodules. Automatic nodule detection is an advanced method that can be used to identify various tissues of abnormalities or nodules. Computer-aided detection (CAD) plays an important role in segmentation and classification problems. Comparative studies and analysis help to identify various aspects of CAD systems that are employed in different scenarios [2; 12]. Another CAD system using an algorithm that is free of parameters was proposed to increase the efficiency of nodule detection [13]. It can be applied to various organs, such as lungs, liver, brain, bones, etc. Recent research declare that they provide efficient and accurate results in the area of segmentation. One of the studies performed the segmentation of lung tumors on the Lotus data set and obtained improved performance by considering a lightweight model design [14]. Convolutional neural networks can also be employed in different scenarios for the identification and classification of lung nodules [15; 16].  
Deep learning problems need a huge data set with accurate and reliable data. Sufficient amounts of reliable data images are necessary to obtain accurate results, but a lack of sufficiently large data sets, high costs to get data from sources, and the issues around dealing with personal information create difficulties in obtaining sufficient amounts of data. As a solution to this problem, many methods have been developed for availing large amounts of data. True transferring weight of a pre-trained model is one of the popular methods for achieving this goal to a certain extent [8; 9]. Another method is data augmentation. Data augmentation is the process of increasing the amount of data available by adding copies of the available data with slight modifications [10]. By using generative adversarial networks, it is possible to generate data artificially. Generative adversarial networks can generate high quality images that can be used instead of real data. In the proposed model, a GAN [3] was used to generate CT images of lungs. Various studies related to this field use the image generation technique. CT images generated from the available authentic data resources have been used for various applications [17; 18; 19; 20; 21]. Detection and segmentation for lung nodules has been performed effectively by using the mask R-CNN algorithm [23]. Lung nodule segmentation for raw thoracic CT scans employed regional CNN effectively for developing a fully automated system for detection and segmentation [24]. Optimized 3D-UNET modeling has also been proposed to segment pulmonary nodules in CT images [25].  
To achieve quality data images, a GAN was used. Authentic and accurate data samples were used here to generate the needed database. The finetuned architecture used real data samples to make the proposed system more stable and efficient. The methods employed, and other specifications, are included in the following sections in detail.