Lung nodule segmentation using Salp Shuffled Shepherd Optimization Algorithm-based Generative Adversarial Network

Abstract

Lung nodule segmentation is an exciting area of research for the effective detection of lung cancer. One of the significant challenges in detecting lung cancer is Accuracy, which is affected due to the visual deviations and heterogeneity in the lung nodules. Hence, to improve the segmentation process's Accuracy, a Salp Shuffled Shepherd Optimization Algorithm-based Generative Adversarial Network (SSSOA-based GAN) model is developed in this research for lung nodule segmentation. The SSSOA is the hybrid optimization algorithm developed by integrating the Salp Swarm Algorithm (SSA) and shuffled shepherd optimization algorithm (SSOA). The artefacts in the input Computed Tomography (CT) image are removed by performing pre-processing with the help of a Gaussian filter. The pre-processed image is subjected to lung lobe segmentation, which is done with the help of deep joint segmentation for segmenting the appropriate regions. The lung nodule segmentation is performed using the GAN. The GAN is trained using the SSSOA to effectively segment the lung nodule from the lung lobe image. The metrics, such as Dice Coefficient, Accuracy, and Jaccard Similarity, are used to evaluate the performance. The developed SSSOA-based GAN method obtained a maximum Accuracy of 0.9387, a maximum Dice Coefficient of 0.7986, and a maximum Jaccard Similarity of 0.8026, respectively, compared with the existing lung nodule segmentation method.