Enhanced Chimp Optimization Algorithm Using Attack Defense Strategy and Golden Update Mechanism for Robust COVID-19 Medical Image Segmentation

Abstract

Medical image segmentation is a powerful and evolving technology in medical diagnosis. In fact, it has been identified as a very effective tool to support and accompany doctors in their fight against the spread of the coronavirus (COVID-19). Various techniques have been utilized for COVID-19 image segmentation, including Multilevel Thresholding (MLT)-based meta-heuristics, which are considered crucial in addressing this issue. However, despite their importance, meta-heuristics have significant limitations. Specifically, the imbalance between exploration and exploitation, as well as premature convergence, can cause the optimization process to become stuck in local optima, resulting in unsatisfactory segmentation results. In this paper, an enhanced War Strategy Chimp Optimization Algorithm (WSChOA) is proposed to address MLT problems. Two strategies are incorporated into the traditional Chimp Optimization Algorithm. Golden update mechanism that provides diversity in the population. Additionally, the attack and defense strategies are incorporated to improve the search space leading to avoiding local optima. The experimental results were conducted by comparing WSChoA with recent and well-known algorithms using various evaluation metrics such as Feature Similarity Index (FSIM), Structural Similarity Index (SSIM), Peak signal-to-Noise Ratio (PSNR), Standard deviation (STD), Freidman Test (FT), and Wilcoxon Sign Rank Test (WSRT). The results obtained by WSChoA surpassed those of other optimization techniques in terms of robustness and accuracy, indicating that it is a powerful tool for image segmentation.