Firefly Competitive Swarm Optimization Based Hierarchical Attention Network for Lung Cancer Detection

Abstract

Lung cancer is a severe disease, which causes high deaths in the world. Earlier discovery of lung cancer is useful to enhance the rate of survival in patients. Computed Tomography (CT) is utilized for determining the tumor and identifying the cancer level in the body. However, the issues of CT images cause less tumor visibility areas and unconstructive rates in tumor regions. This paper devises an optimization-driven technique for classifying lung cancer. The CT image is utilized for determining the position of the tumor. Here, the CT image undergoes segmentation, which is performed using the DeepJoint model. Furthermore, the feature extraction is carried out, wherein features such as local ternary pattern-based features, Histogram of Gradients (HoG) features, and statistical features, like variance, mean, kurtosis, energy, entropy, and skewness. The categorization of lung cancer is performed using Hierarchical Attention Network (HAN). The training of HAN is carried out using proposed Firefly Competitive Swarm Optimization (FCSO), which is devised by combining firefly algorithm (FA), and Competitive Swarm Optimization (CSO). The proposed FCSO-based HAN provided effective performance with high accuracy of 91.3%, sensitivity of 88%, and specificity of 89.1%.