Said Yahia, Ayachi Riadh, Afif Mouna, Saidani Taoufik, Alanezi Saleh T, Saidani Oumaima, Algarni Ali Delham
Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, 73213, Saudi Arabia.
Faculty of Sciences of Monastir, University of Monastir, Monastir, 5019, Tunisia.
Sci Rep. 2025 Jul 2;15(1):23058. doi: 10.1038/s41598-025-08116-w.
Lung cancer remains the leading cause of cancer-related mortality worldwide, necessitating accurate and efficient diagnostic tools to improve patient outcomes. Lung segmentation plays a pivotal role in the diagnostic pipeline, directly impacting the accuracy of disease detection and treatment planning. This study presents an advanced AI-driven framework, optimized through genetic algorithms, for precise lung segmentation in early cancer diagnosis. The proposed model builds upon the UNET3 + architecture and integrates multi-scale feature extraction with enhanced optimization strategies to improve segmentation accuracy while significantly reducing computational complexity. By leveraging genetic algorithms, the framework identifies optimal neural network configurations within a defined search space, ensuring high segmentation performance with minimal parameters. Extensive experiments conducted on publicly available lung segmentation datasets demonstrated superior results, achieving a dice similarity coefficient of 99.17% with only 26% of the parameters required by the baseline UNET3 + model. This substantial reduction in model size and computational cost makes the system highly suitable for resource-constrained environments, including point-of-care diagnostic devices. The proposed approach exemplifies the transformative potential of AI in medical imaging, enabling earlier and more precise lung cancer diagnosis while reducing healthcare disparities in resource-limited settings.
肺癌仍然是全球癌症相关死亡的主要原因，因此需要准确有效的诊断工具来改善患者预后。肺部分割在诊断流程中起着关键作用，直接影响疾病检测和治疗规划的准确性。本研究提出了一种先进的人工智能驱动框架，通过遗传算法进行优化，用于早期癌症诊断中的精确肺部分割。所提出的模型基于UNET3 +架构构建，并将多尺度特征提取与增强的优化策略相结合，以提高分割精度，同时显著降低计算复杂度。通过利用遗传算法，该框架在定义的搜索空间内识别最优神经网络配置，以最少的参数确保高分割性能。在公开可用的肺部分割数据集上进行的大量实验显示了优异的结果，仅使用基线UNET3 +模型所需参数的26%就实现了99.17%的骰子相似系数。模型大小和计算成本的大幅降低使得该系统非常适合资源受限的环境，包括即时诊断设备。所提出的方法体现了人工智能在医学成像中的变革潜力，能够实现更早、更精确的肺癌诊断，同时减少资源有限环境中的医疗差距。
