Computational Intelligence Re-meets Medical Image Processing Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases BACKGROUND: In the last decades, new optimization methods based on the nature's intelligence were developed. These metaheuristics can find a nearly optimal solution faster than other traditional algorithms even for high-dimensional optimization problems. All these algorithms have a similar structure, the difference being made by the strategies used during the evolutionary process. OBJECTIVES A set of three nature-inspired algorithms, including Cuckoo Search algorithm (CSA), Particle Swarm Optimization (PSO), and Multi-Swarm Optimization (MSO), are compared in terms of strategies used in the evolutionary process and also of the results obtained in case of particular optimization problems. METHODS The three algorithms were applied for biomedical image registration (IR) and compared in terms of performances. The expected geometric transform has seven parameters and is composed of rotation against a point in the image, scaling on both axis with different factors, and translation. RESULTS The evaluation consisted of 25 runs of each IR procedure and revealed that (1) PSO offers the most precise solutions; (2) CSA and MSO are more stable in the sense that their solutions are less scattered; and (3) MSO and PSO have a higher convergence speed. CONCLUSIONS The evaluation of PSO, MSO, and CSA was made for multimodal IR problems. It is possible that for other optimization problems and also for other settings of the optimization algorithms, the results can be different. Therefore, the nature-inspired algorithms demonstrated their efficacy for this class of optimization problems.

中文翻译：

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生物医学图像配准中某些自然启发式优化元启发式方法的比较。

计算智能重新满足用于弥漫性肺疾病诊断的机器学习算法的医学图像处理分析背景：在过去的几十年中，基于自然智能的新的优化方法得到了发展。即使对于高维优化问题，这些元启发式算法也可以比其他传统算法更快地找到几乎最优的解决方案。所有这些算法都具有相似的结构，不同之处在于进化过程中使用的策略。目的根据进化过程中使用的策略以及结果，比较了三种受自然启发的算法，包括布谷鸟搜索算法（CSA），粒子群优化（PSO）和多群优化（MSO）。在遇到特殊优化问题时获得。方法将这三种算法应用于生物医学图像配准（IR）并进行性能比较。预期的几何变换具有七个参数，并且包括针对图像中某个点的旋转，使用不同因子在两个轴上缩放以及平移。结果评估由每个IR程序的25次运行组成，并显示（1）PSO提供了最精确的解决方案；（2）CSA和MSO在解决方案分散性方面更稳定；（3）MSO和PSO的收敛速度较高。结论对多模式红外问题进行了PSO，MSO和CSA评估。对于其他优化问题以及优化算法的其他设置，结果可能会有所不同。因此，