Refining COVID-19 Lesion Segmentation in Lung CT Scans Using Swarm Intelligence and Evolutionary Algorithms

IF 2.1 3区医学 Q2 SURGERY International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2025-02-07 DOI:10.1002/rcs.70044

Wafa Gtifa, Marwa Fradi, Anis Sakly, Mohsen Machhout

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Abstract

Background

Accurately identifying lung lesions in CT (Computed Tomography) scans remains crucial during the Coronavirus Disease 2019 (COVID-19) pandemic. Swarm intelligence algorithms offer promising tools for this purpose.

Methods

This study compares four swarm intelligence algorithms Gravitational Search Algorithm (GSA), Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for segmenting COVID-19 lung lesions.

Results

GA, GSA, and BFOA achieved accuracies exceeding 90.5%, while the PSO algorithm further improved segmentation accuracy, reaching 91.45%, with an exceptional F1 score of 95.54%. Overall, the approach achieved up to 99% segmentation accuracy.

Conclusions

The findings demonstrate the effectiveness of swarm and evolutionary algorithms in segmenting COVID-19 lesions, contributing to enhanced diagnostic accuracy and treatment efficiency.

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利用群智能和进化算法改进肺部CT扫描中COVID-19病变分割

在2019冠状病毒病（COVID-19）大流行期间，在CT扫描中准确识别肺部病变仍然至关重要。群体智能算法为这一目的提供了很有前途的工具。方法比较重力搜索算法（GSA）、细菌觅食优化算法（BFOA）、遗传算法（GA）和粒子群算法（PSO） 4种群体智能算法对COVID-19肺病变的分割效果。结果GA、GSA和BFOA的分割准确率均超过90.5%，而PSO算法进一步提高了分割准确率，达到91.45%，F1得分达到95.54%。总体而言，该方法实现了高达99%的分割准确率。结论采用群算法和进化算法对COVID-19病变进行分割是有效的，有助于提高诊断准确性和治疗效率。

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来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.