GA-AGN: A generative adversarial network and attention gated network model for enhanced lung cancer detection using chest CT scans

One of the most dangerous diseases that affect people worldwide is lung cancer. The survival rate is minimal, because of the complexity in identifying lung cancer at developed stages. Henceforth, earlier detection of lung cancer is significant. Several Machine Learning (ML) approaches have been modeled for lung cancer recognition with the advent of Artificial Intelligence. However, small-scale datasets and deprived generalizability to recognize unknown data are considered challenges in lung cancer detection. This work proposes an advanced deep learning model, named Generative Adversarial Network- Attention Gated Network (GA-AGN), which is the integration of Generative Adversarial Network (GAN) and Attention Gated Network (AGN). Initially, the chest CT scan images are subjected to the pre-processing phase, where image resizing and normalization are used to preprocess the images. Then, the data augmentation is performed using the GAN model that is trained by Elk Herd Optimizer (EHO). Subsequently, lung cancer detection is done by means of GA-AGN model. Ultimately analysis is performed by using three measures, like accuracy, sensitivity as well as specificity with values of 0.938, 0.948 and 0.927. The overall analysis states that the proposed model attained better outcomes than the conventional models.