AI-driven genetic algorithm-optimized lung segmentation for precision in early lung cancer diagnosis.
Authors
Affiliations (7)
Affiliations (7)
- Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, 73213, Saudi Arabia. [email protected].
- Faculty of Sciences of Monastir, University of Monastir, Monastir, 5019, Tunisia.
- Laboratory of Condensed Matter and Nanosciences, Faculty of Sciences of Monastir, University of Monastir, Monastir, 5019, Tunisia.
- Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, 73213, Saudi Arabia.
- Department of Physics, College of Science, Northern Border University, Arar, 91431, Saudi Arabia.
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
- Computer Science and Artificial Intelligence Department, College of Computing and Information Technology, University of Bisha, Bisha, 14174, Saudi Arabia.
Abstract
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.