An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm

Skin cancer is one of the worst cancers nowadays that poses a severe threat to the health and safety of individuals. Therefore, skin cancer classification and early diagnosis are recommended to preserve human life. Multilevel thresholding image segmentation is well-known and influential technique for extracting regions of interest from skin cancer images to improve the classification process. Therefore, this paper proposes an efficient version of the recently developed golden jackal optimization (GJO) algorithm, the opposition-based golden jackal optimizer (IGJO). The IGJO algorithm is used to solve the multilevel thresholding problem using Otsu's method as an objective function. The proposed algorithm is compared with seven other meta-heuristic algorithms: whale optimization algorithm, seagull optimization algorithm, salp swarm algorithm, Harris hawks optimization, artificial gorilla troops optimizer, marine predators' algorithms, and original GJO algorithm. The performance of the proposed algorithm is evaluated using four popular performance measures: peak signal-to-noise ratio, structure similarity index, feature similarity index, and mean square error. Experimental results show that the proposed algorithm outperforms other alternative algorithms in terms of PSNR, SSIM, FSIM, and MSE segmentation metrics and effectively resolves the segmentation problem.