Optimized lung cancer detection by amended whale optimizer and rough set theory

The current paper proposes a new hierarchical procedure for efficient diagnosis of lung cancer computed tomography (CT) images. Here, after noise removal based on median filtering, a contrast enhancement based on general histogram equalization (GHE) has been utilized. Then, a modified version of K-means clustering has been used for the area of interest segmentation in the CT images. The major characteristics of the segmented images have been selected during an optimization technique and the outputs are injected into an optimized radial basis function (RBF) network for the final classification. Optimization in the classification stage and feature selection is by an improved metaheuristic technique, called Amended Whale Optimization Algorithm was proposed. The designed method is then applied to “The RIDER Lung CT” database and its achievements are validated by several latest techniques to show its higher efficacy.