###### Abstract  
Lung cancer is seen as one of the most common lung diseases. For the patients having symptoms, the presence of lung nodules is checked by using various imaging techniques. Pulmonary nodules are detected in most of the cases having symptoms. But identifying the type of the nodule and the categorization still remains as a challenge. After confirming the presence of a nodule (benign or malignant) it takes several other steps to identify its characteristics. Improved imaging methods produce results within a short span of time. Research works are being conducted to increase the overall efficiency of the system. The proposed system considers authentic data sources for the study. The benign and malignant samples are considered for the generation of realistic large image sets. The generation of a large data set with the help of a generative adversarial network (GAN) is the first part of the work. The generated images using GAN cannot be differentiated from the original images even by a trained radiologist. This proves the importance of images generated using GAN. A GAN is able to generate 1024 \(\times\) 1024 resolutions for natural images. Real data images are used to finetune the SegNet output. Through transfer learning, these weights are transferred to the system for segmentation of the images. The training process use real and generated images, which improve theefficiency of the network. The original data from LUNA 16 was used to further generate benign and malignant samples using GAN. A total of 440 images and their augmented images were used for training the GAN, and it generated 1,001,000 images. Hence the overall efficiency of the system was improved. To verify the results, the same various combinations and methods were considered and tabulated with various parameters. Methods with SegNet, GAN, and other combinations were evaluated to verify the efficiency of the system. Receiver operating characteristics were also plotted and compared with the area under the curve for verification of the results. pulmonary nodule; SegNet; adversarial network; transfer learning; GAN +
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generative adversarial network. With the help of encoder and decoder networks available in a semantic segmentation model, the lung nodule classification was done efficiently. Similarly, the proposed system used SegNet-based architecture with five convolution layers over the U Net-based existing architecture with three convolution layers. Hence, the proposed system resolves the issue related to the availability of large amounts of data for training purposes. The transfer learning process in the proposed system adds the advantage of information transfer, in the form of weights, to the new network. The transfer learning process is another technique included in the proposed system for making the training and segmentation process easy and more efficient. Thus, the proposed system overcomes the drawbacks of the existing system and proposes an efficient method which involves the use of a GAN for image generation, transfer learning techniques for using the weights of a pre-trained network, and SegNet for the segmentation process [3]. The proposed method of having a GAN along with image augmentation was compared with other methods. GAN with SegNet showed very small deviation from the proposed method. This clearly shows the advantage of using GAN for generating images for nodule segmentation. The method was also compared with other encoder and decoder architectures. Medical imaging is an important tool for the diagnosis and treatment of various diseases. In particular, computed tomography (CT) imaging is widely used for the diagnosis of lung cancer, which is one of the leading causes of cancer-related deaths worldwide. Segmentation of the pulmonary nodules in CT images is a critical step in the diagnosis of lung cancer. However, manual segmentation of pulmonary nodules is a time-consuming and error-prone task, and may not be feasible for large-scale studies.