A Novel Approach for Evaluating the Performance of a Computer-Assisted System for the Diagnosis of Lung Cancer Using a Combination of Image Processing and Machine Learning Techniques

• The proposed CAD system achieves high sensitivity (94.2%) and specificity (91.8%) for lung nodule detection, with an AUC of 0.97, outperforming existing methods. • Integration of multi-scale feature extraction and hybrid attention mechanisms enhances the model's ability to identify subtle nodules, reducing false positives. • The system demonstrates robust performance across diverse CT datasets, indicating its potential for clinical deployment in various healthcare settings. • The study provides a comprehensive evaluation framework, including cross-validation and external validation, ensuring reliability and generalizability of the results.